RESEARCH CENTRE

Inria Centre
at Université Côte d’Azur

2023
ACTIVITY REPORT

Project-Team

EPIONE

E-Patient: Images, Data & MOdels for
e-MediciNE

DOMAIN

Digital Health, Biology and Earth

THEME

Computational Neuroscience and
Medicine

Contents

Project-Team EPIONE

1 Team members, visitors, external collaborators

2 Overall objectives

2.1 Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 Research program

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Biomedical Image Analysis & Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Imaging & Phenomics, Biostatistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Computational Anatomy, Geometric Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5 Computational Physiology & Image-Guided Therapy . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . .
3.6 Computational Cardiology & Image-Based Cardiac Interventions

4 Application domains

5 Social and environmental responsibility

5.1 Footprint of research activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 Highlights of the year

6.1 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7 New software, platforms, open data

9
9
7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.1 CardiacSegmentationPropagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
7.1.2 CardiacMotionFlow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
7.1.3 MedINRIA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
7.1.4 GP-ProgressionModel
7.1.5 Music . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
7.1.6 SOFA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
7.1.7 geomstats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
7.1.8 MC-VAE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
7.1.9 SOFA-CardiacReduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.1.10 Fed-BioMed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.1.11 EchoFanArea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
7.1.12 ProMFusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
7.1.13 SimulAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
7.2 Open data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

8 New results

15
8.1 Medical Image Analysis & Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
8.1.1 AI-based Real-time Diagnostic Aid for Abdominal Organs in Ultrasound . . . . . . . 15
8.1.2 Machine Learning for MR-TRUS Prostate Image Fusion . . . . . . . . . . . . . . . . . 16
8.1.3 Prostate and Humerus Shape Analysis
. . . . . . . . . . . . . . . . . . . . . . . . . . . 16
8.1.4 Automatic Segmentation of the Vascular System to Enhance AI-based Decision

Support System for Peripheral Artery Disease . . . . . . . . . . . . . . . . . . . . . . . 16
8.1.5 AI-Based Diagnosis of Prostate Cancer from Multiparametric MRI . . . . . . . . . . . 17
8.1.6 Thyrosonics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
8.1.7 AI-based Selection of Imaging and Biological Markers Predictive of Treatment Re-

sponse in Lung Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
8.2 Imaging & Phenomics, Biostatistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
8.2.1 Development of Genome-to-Phenome Association Methods Using Biologically In-

spired Constraints

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

8.2.2 Exploring Latent Dynamical Models for the Analysis of High-dimensional and Het-

erogeneous Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
8.3 Computational Anatomy & Geometric Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . 22
8.3.1 Riemannian and Sub-riemannian Methods for Dimension Reduction . . . . . . . . . 22
8.3.2 Towards a BCH Formula on the Diffeomorphism Group with a Right-invariant Metric 23
8.3.3 Graph Alignment Exploiting the Spatial Organization Improves the Similarity of Brain

Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
8.3.4 Towards Barycentric Subspace Analysis for Graphs . . . . . . . . . . . . . . . . . . . . 24
8.3.5 Riemannian Geometry and Geometric Statistics: from Basic Theory to Implementa-

tion with Geomstats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
8.3.6 Geometry of Sample Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
8.3.7 Barycentric Subspaces for Geometric Learning . . . . . . . . . . . . . . . . . . . . . . 26
8.3.8 Riemannian Logarithm on Flag Manifolds . . . . . . . . . . . . . . . . . . . . . . . . . 27
8.3.9 Riemannian and Stratified Geometries on Covariance and Correlation Matrices . . . 27
. . . . . . . . . . . . . . . 28
8.4.1 Causal Data Analysis for Torsade de Pointes Risk Assessment . . . . . . . . . . . . . . 28
8.4.2 Efficient Patient-Specific Simulations of Ventricular Tachycardia Based on Computed

8.4 Computational Cardiology & Image-Based Cardiac Interventions

Tomography-defined Wall Thickness Heterogeneity . . . . . . . . . . . . . . . . . . . 29

8.4.3 Personalization of Cardiac Models for the Prediction of Cardiac Resynchronization

Therapy Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

8.4.4 Cardiac Electrophysiology Model Correction Using Differentiable Physics and Deep

Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
8.4.5 Outcome Prediction in Cardiology: a Practical Guide . . . . . . . . . . . . . . . . . . . 32
8.4.6 Inducibility of Ventricular Tachycardia by Means of Different Stimulation Protocols:

an Electrophysiology Simulation Study . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

8.4.7 Unsupervised Polyaffine Transformation Learning for Echocardiography Motion

Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
8.5 Multi-centric data and Federated Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
8.5.1 Federated Missing Data Imputation: Towards Federated Pre-processing . . . . . . . 34
8.5.2 Real-world Deployment of Federated Learning in Biomedical Research Consortia

with Fed-BioMed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
8.5.3 Reliability and Robustness of Federated Learning in Practical Applications . . . . . . 36
8.5.4 Multi-centric AI-based Framework for Prostate Cancer Patients Management on

Active Surveillance Through Robust Federated Learning . . . . . . . . . . . . . . . . . 37

8.5.5 StratifyAging: Interoperatibility of Clinical Studies and Routine Care for the Advent

of a Stratified Medicine of Aging Using Clinical, Imaging, and Omics Data . . . . . . 39

8.5.6 Federated Data Harmonization in Biomedical Research Using Mixed Effects Models:

a Focus on Conditional Variational Autoencoders . . . . . . . . . . . . . . . . . . . . . 39
8.5.7 Data Structuration and Security in Large-scale Collaborative Healthcare Data Analysis 41
8.5.8 Federated Unlearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

9 Bilateral contracts and grants with industry

42
9.1 Bilateral contracts with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
9.1.1 Koelis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
9.1.2 Accenture Labs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
9.1.3 MyDataModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
9.1.4 Microport CRM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
9.1.5 ExactCure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
9.1.6 Spin-off company inHEART . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

10 Partnerships and cooperations

43
10.1 International collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
10.1.1 Participation in other International Programs . . . . . . . . . . . . . . . . . . . . . . . 44
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
10.2.1 Visits of international scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

10.2 International research visitors

10.3 European initiatives

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
10.3.1 Horizon Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
10.3.2 H2020 projects
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
10.3.3 Other european programs/initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
10.4 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
10.4.1 PEPR Digital Health ChroniCardio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
10.4.2 PEPR Digital Health Rewind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
10.4.3 PEPR Digital Health Secure, safe and fair machine learning for healthcare . . . . . . 49
10.4.4 PEPR Digital Health Stratigy Aging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
10.4.5 Other national initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
10.5 Regional initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

11 Dissemination

51
11.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
11.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
11.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
11.1.3 Journal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
11.1.4 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
11.1.5 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
11.1.6 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
11.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
11.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
11.2.2 Supervision: defended PhDs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
11.2.3 Supervision: ongoing PhDs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
11.2.4 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
11.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
11.3.1 Articles and contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
11.3.2 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

12 Scientific production

57
12.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
12.2 Publications of the year

Project EPIONE

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Project-Team EPIONE

Creation of the Project-Team: 2018 May 01

Keywords

Computer sciences and digital sciences

A3.3. – Data and knowledge analysis

A3.4. – Machine learning and statistics

A4.3. – Cryptography

A4.4. – Security of equipment and software

A4.8. – Privacy-enhancing technologies

A5.2. – Data visualization

A5.3. – Image processing and analysis

A5.4. – Computer vision

A5.6. – Virtual reality, augmented reality

A5.9. – Signal processing

A6.1. – Methods in mathematical modeling

A6.2. – Scientific computing, Numerical Analysis & Optimization

A6.3. – Computation-data interaction

A8.3. – Geometry, Topology

A9. – Artificial intelligence

A9.2. – Machine learning

A9.3. – Signal analysis

A9.6. – Decision support

A9.7. – AI algorithmics

A9.9. – Distributed AI, Multi-agent

A9.10. – Hybrid approaches for AI

Other research topics and application domains

B2.2. – Physiology and diseases

B2.3. – Epidemiology

B2.4. – Therapies

B2.6. – Biological and medical imaging

B2.6.1. – Brain imaging

B2.6.2. – Cardiac imaging

B2.6.3. – Biological Imaging

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Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Nicholas Ayache [Team leader, Inria, Senior Researcher, HDR]

• Irene Balelli [Inria, ISFP]

• James Benn [Inria, Starting Research Position]

• Hervé Delingette [Inria, Senior Researcher, HDR]

• Marco Lorenzi [Inria, Researcher, HDR]

• Marta Nunez Garcia [Inria, Starting Research Position, until Feb 2023]

• Guillaume Olikier [Inria, Starting Research Position, from Oct 2023]

• Xavier Pennec [Inria, Senior Researcher, HDR]

• Maxime Sermesant [Inria, Senior Researcher, HDR]

Post-Doctoral Fellows

• Safaa Al Ali [Inria, Post-Doctoral Fellow]

• Anna Calissano [Inria, Post-Doctoral Fellow, until Jun 2023]

• Marie Deprez [Inria, until Apr 2023]

• Amel Karoui [Inria, Post-Doctoral Fellow, until Nov 2023]

• Buntheng Ly [Inria, until Mar 2023]

• Ghiles Reguig [Inria, Post-Doctoral Fellow]

• Jesus Jairo Rodriguez Padilla [Inria]

PhD Students

• Morten Akhoj Pedersen [Inria, until Oct 2023]

• Olivier Bisson [Inria, from Oct 2023]

• Gaetan Desrues [Inria, until Mar 2023]

• Federica Facente [Inria, from Sep 2023]

• Zhijie Fang [Inria, until Sep 2023]

• Samy Ferrat [Inria, from Dec 2023]

• Yann Fraboni [Accenture Labs Sophia Antipolis, CIFRE, until Jun 2023]

• Erwan Gaymard [EXACTCURE, CIFRE, until Jun 2023]

• Sebastien Goffart [CHU NICE, from Sep 2023]

• Lisa Guzzi [UNS]

• Dimitri Hamzaoui [Inria, until Jun 2023]

• Josquin Harrison [Inria]

• Etrit Haxholli [Inria, until Oct 2023]

Project EPIONE

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• Lucia Innocenti [Inria]

• Victoriya Kashtanova [Inria, until Jun 2023]

• Manasi Kattel [Inria, from Dec 2023]

• Mengda Li [Inria, from Sep 2023]

• Huiyu Li [Inria]

• Elodie Maignant [Inria]

• Maelis Morier [Inria, from Nov 2023]

• Evariste Njomgue Fotso [Inria, from Nov 2023]

• Santiago Smith Silva Rincon [Université Côte d’Azur, until Jul 2023]

• Rafael Luis Soares Da Costa E Silva [Inria, from Dec 2023]

• Hari Sreedhar [Université Côte d’Azur]

• Tom Szwagier [Inria]

• Riccardo Taiello [Inria]

• Paul Tourniaire [Inria, until May 2023]

• Martin Van Waerebeke [Inria, from Jun 2023 until Nov 2023]

• Yingyu Yang [Inria]

Technical Staff

• Francesco Cremonesi [Inria, Engineer]

• Luis Gomes Pereira [Inria, Engineer]

• Marco Milanesio [Université Côte d’Azur, Engineer]

• Anaëlle Zanella [Inria, Engineer]

Interns and Apprentices

• Amele Ahraoui [inHEART, Intern, from Feb 2023 until Jun 2023]

• Olivier Bisson [Inria, Intern, from Apr 2023 until Sep 2023]

• Samy Ferrat [Inria, Intern, from Jun 2023 until Nov 2023]

• Bhargav Ramudu Manam [Inria, Intern, from Apr 2023 until Sep 2023]

• Evariste Njomgue Fotso [Inria, Intern, from Apr 2023 until Aug 2023]

• Rafael Luis Soares Da Costa E Silva [INRIA, from May 2023 until Nov 2023]

Administrative Assistant

• Isabelle Strobant [Inria]

4

Inria Annual Report 2023

Visiting Scientists

• Federica Cruciani [UNIVR ITALIE, until Apr 2023]

• Sébastien Frey [CHU NICE, from Dec 2023]

• Hervé Lombaert [ETS MONTREAL, from Nov 2023]

• Hervé Lombaert [ETS MONTREAL, until Feb 2023]

• Mihaela Pop [Inria, from Aug 2023]

• Mihaela Pop [Inria, until Jun 2023]

External Collaborator

• Nicolas Cedilnik [inHEART]

2 Overall objectives

2.1 Description

Our long-term goal is to contribute to the development of what we call the e-patient (digital patient) for
e-medicine (digital medicine).

Figure 1: The e-patient for e-medicine

• the e-patient (or digital patient) is a set of computational models of the human body able to describe
and simulate the anatomy and the physiology of the patient’s organs and tissues, at various scales,
for an individual or a population. The e-patient can be seen as a framework to integrate and analyze
in a coherent manner the heterogeneous information measured on the patient from disparate
sources: imaging, biological, clinical, sensors, ...

• e-medicine (or digital medicine) is defined as the computational tools applied to the e-patient to
assist the physician and the surgeon in their medical practice, to assess the diagnosis/prognosis,
and to plan, control and evaluate the therapy.

The models that govern the algorithms designed for e-patients and e-medicine come from various
disciplines: computer science, mathematics, medicine, statistics, physics, biology, chemistry, etc. The
parameters of those models must be adjusted to an individual or a population based on the available
images, signals and data. This adjustment is called personalization and usually requires solving difficult

Project EPIONE

5

inverse problems. The overall picture of the construction of the personalized e-patient for e-medicine
was presented at the College de France through an inaugural lecture and a series of courses and seminars
(fr), concluded by an international workshop.

2.2 Organization

The research organization in our field is often built on a virtuous triangle. On one vertex, academic
research requires multidisciplinary collaborations associating informatics and mathematics to other
disciplines: medicine, biology, physics, chemistry ... On a second vertex, a clinical partnership is required
to help defining pertinent questions, to get access to clinical data, and to clinically evaluate any proposed
solution. On the third vertex, an industrial partnership can be introduced for the research activity itself,
and also to transform any proposed solution into a validated product that can ultimately be transferred
to the clinical sites for an effective use on the patients.

Figure 2: A pluridisciplinary research triangle

Keeping this triangle in mind, we choose our research directions within a virtuous circle: we look
at difficult problems raised by our clinical or industrial partners, and then try to identify some classes
of generic fundamental/theoretical problems associated to their resolution. We also study some funda-
mental/theoretical problems per se in order to produce fundamental scientific advances that can help in
turn to promote new applications.

3 Research program

3.1 Introduction

Our research objectives are organized along 5 scientific axes:

1. Biomedical Image Analysis & Machine Learning

2. Imaging & Phenomics, Biostatistics

3. Computational Anatomy, Geometric Statistics

4. Computational Physiology & Image-Guided Therapy

5. Computational Cardiology & Image-Based Cardiac Interventions

For each scientific axis, we introduce the context and the long term vision of our research.

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Inria Annual Report 2023

Figure 3: Epione’s five main research axes

3.2 Biomedical Image Analysis & Machine Learning

The long-term objective of biomedical image analysis is to extract, from biomedical images, pertinent
information for the construction of the e-patient and for the development of e-medicine. This relates
to the development of advanced segmentation and registration of images, the extraction of image
biomarkers of pathologies, the detection and classification of image abnormalities, the construction of
temporal models of motion or evolution from time-series of images, etc.

In addition, the growing availability of very large databases of biomedical images, the growing power
of computers and the progress of machine learning (ML) approaches have opened up new opportunities
for biomedical image analysis.

This is the reason why we decided to revisit a number of biomedical image analysis problems with ML
approaches, including segmentation and registration problems, automatic detection of abnormalities,
prediction of a missing imaging modality, etc. Not only those ML approaches often outperform the
previous state-of-the-art solutions in terms of performances (accuracy of the results, computing times),
but they also tend to offer a higher flexibility like the possibility to be transferred from one problem to
another one with a similar framework. However, even when successful, ML approaches tend to suffer
from a lack of explanatory power, which is particularly annoying for medical applications. We also plan to
work on methods that can interpret the results of the ML algorithms that we develop.

3.3 Imaging & Phenomics, Biostatistics

The human phenotype is associated with a multitude of heterogeneous biomarkers quantified by imaging,
clinical and biological measurements, reflecting the biological and patho-physiological processes govern-
ing the human body, and essentially linked to the underlying individual genotype. In order to deepen
our understanding of these complex relationships and better identify pathological traits in individuals
and clinical groups, a long-term objective of e-medicine is therefore to develop the tools for the joint
analysis of this heterogeneous information, termed Phenomics, within the unified modeling setting of the
e-patient.

To date the most common approach to the analysis of the joint variation between the structure and
function of organs represented in medical images, and the classical -omics modalities from biology,
such as genomics or lipidomics, is essentially based on the massive univariate statistical testing of single
candidate features out of the many available. This is for example the case of genome-wide association
studies (GWAS) aimed at identifying statistically significant effects in pools consisting of up to millions

Project EPIONE

7

of genetics variants. Such approaches have known limitations such as multiple comparison problems,
leading to underpowered discoveries of significant associations, and usually explain a rather limited
amount of data variance. Although more sophisticated machine learning approaches have been proposed,
the reliability and generalization of multivariate methods is currently hampered by the low sample size
relatively to the usually large dimension of the parameters space.

To address these issues this research axis investigates novel methods for the integration of this het-
erogeneous information within a parsimonious and unified multivariate modeling framework. The
cornerstone of the project consists in achieving an optimal trade-off between modeling flexibility and
ability to generalize on unseen data by developing statistical learning methods informed by prior inform-
ation, either inspired by "mechanistic" biological processes, or accounting for specific signal properties
(such as the structured information from spatio-temporal image time series). Finally, particular attention
will be paid to the effective exploitation of the methods in the growing Big Data scenario, either in the
meta-analysis context, or for the application in large datasets and biobanks.

Federated learning in multi-centric studies. The current research scenario is characterized by
medium/small scale (typically from 50 to 1000 patients) heterogeneous datasets distributed across
centres and countries. The straightforward extension of learning algorithms successfully applied to big
data problems is therefore difficult, and specific strategies need to be envisioned in order to optimally
exploit the available information. To address this problem, we focus on learning approaches to jointly
model clinical data localized in different centres. This is an important issue emerging from recent large-
scale multi-centric imaging-genetics studies in which partners can only share model parameters (e.g.
regression coefficients between specific genes and imaging features), as represented for example by
the ENIGMA imaging-genetics study, led by the collaborators at University of Southern California. This
problem requires the development of statistical methods for federated model estimation, in order to
access data hosted in different clinical institutions by simply transmitting the model parameters, that
will be in turn updated by using the local available data. This approach is extended to the definition
of stochastic optimization strategies in which model parameters are optimized on local datasets, and
then summarized in a meta-analysis context. Finally, this project studies strategies for aggregating the
information from heterogeneous datasets, accounting for missing modalities due to different study
design and protocols. The developed methodology finds important applications within the context of Big
Data, for the development of effective learning strategies for massive datasets in the context of medical
imaging (such as with the UK biobank), and beyond.

3.4 Computational Anatomy, Geometric Statistics

Computational anatomy is an emerging discipline at the interface of geometry, statistics and image
analysis which aims at developing algorithms to model and analyze the biological shape of tissues
and organs. The goal is not only to establish generative models of organ anatomies across diseases,
populations, species or ages but also to model the organ development across time (growth or aging) and
to estimate their variability and link to other functional, genetic or structural information. Computational
anatomy is a key component to support computational physiology and is evidently crucial for building
the e-patient and to support e-medicine.

Pivotal applications include the spatial normalization of subjects in neuroscience (mapping all the
anatomies into a common reference system) and atlas to patient registration to map generic knowledge
to patient-specific data. Our objectives will be to develop new efficient algorithmic methods to address
the emerging challenges described below and to generate precise specific anatomical model in particular
for the brain and the heart.

The objects of computational anatomy are often shapes extracted from images or images of labels
(segmentation). The observed organ images can also be modeled using registration as the random
diffeomorphic deformation of an unknown template (i.e. an orbit). In these cases as in many other
applications, invariance properties lead us to consider that these objects belong to non-linear spaces
that have a geometric structure. Thus, the mathematical foundations of computational anatomy rely on
statistics on non-linear spaces.

Geometric Statistics aim at studying this abstracted problem at the theoretical level. Our goal is to
advance the fundamental knowledge in this area, with potential applications to new areas outside of
medical imaging. Beyond the now classical Riemannian spaces, we aim at developing the foundations of

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Inria Annual Report 2023

statistical estimation on affine connection spaces (e.g. Lie groups), quotient and stratified metric spaces
(e.g. orbifolds and tree spaces). In addition to the curvature, one of the key problem is the introduction of
singularities at the boundary of the regular strata (non-smooth and non-convex analysis).

A second objective is to develop parametric and non-parametric dimension reduction methods in
non-linear space. An important issue is to estimate efficiently not only the model parameters (mean
point, subspace, flag) but also their uncertainty. We also want to quantify the influence of curvature and
singularities on non-asymptotic estimation theory since we always have a finite (and often too limited)
number of samples. A key challenge in developing such a geometrization of statistics will not only be to
unify the theory for the different geometric structures, but also to provide efficient practical algorithms to
implement them.

A third objective is to learn the geometry from the data. In the high dimensional but low sample
size (small data) setting which is the common situation in medical data, we believe that invariance
properties are essential to reasonably interpolate and approximate. New apparently antagonistic notions
like approximate invariance could be the key to this interaction between geometry and learning.

Beyond the traditional statistical survey of the anatomical shapes that is developed in computational
anatomy above, we intend to explore other application fields exhibiting geometric but non-medical data.
For instance, applications can be found in Brain-Computer Interfaces (BCI), tree-spaces in phylogenetics,
Quantum Physics, etc.

3.5 Computational Physiology & Image-Guided Therapy

Computational Physiology aims at developing computational models of human organ functions, an im-
portant component of the e-patient , with applications in e-medicine and more specifically in computer-
aided prevention, diagnosis, therapy planning and therapy guidance. The focus of our research is on
descriptive (allowing to reproduce available observations), discriminative (allowing to separate two
populations), and above all predictive models which can be personalized from patient data including
medical images, biosignals, biological information and other available metadata. A key aspect of this
scientific axis is therefore the coupling of biophysical models with patient data which implies that we are
mostly considering models with relatively few and identifiable parameters. To this end, data assimilation
methods aiming at estimating biophysical model parameters in order to reproduce available patient data
are preferably developed as they potentially lead to predictive models suitable for therapy planning.

Previous research projects in computational physiology have led us to develop biomechanical models
representing quasi-static small or large soft tissue deformations (e.g. liver or breast deformation after sur-
gery), mechanical growth or atrophy models (e.g. simulating brain atrophy related to neurodegenerative
diseases), heat transfer models (e.g. simulating radiofrequency ablation of tumors), and tumor growth
models (e.g. brain or lung tumor growth).

To improve the data assimilation of biophysical models from patient data, a long term objective
of our research will be to develop joint imaging and biophysical generative models in a probabilistic
framework which simultaneously describe the appearance and function of an organ (or its pathologies)
in medical images. Indeed, current approaches for the personalization of biophysical models often
proceed in two separate steps. In a first stage, geometric, kinematic or/ functional features are first
extracted from medical images. In a second stage, they are used by personalization methods to optimize
model parameters in order to match the extracted features. In this process, subtle information present
in the image which could be informative for biophysical models is often lost which may lead to limited
personalization results. Instead, we propose to develop more integrative approaches where the extraction
of image features would be performed jointly with the model parameter fitting. Those imaging and
biophysical generative models should lead to a better understanding of the content of images, to a better
personalization of model parameters and also better estimates of their uncertainty.

3.6 Computational Cardiology & Image-Based Cardiac Interventions

Computational Cardiology has been an active research topic within the Computational Anatomy and
Computational Physiology axes of the previous Asclepios project, leading to the development of personal-
ized computational models of the heart designed to help characterizing the cardiac function and predict
the effect of some device therapies like cardiac resynchronization or tissue ablation . This axis of research

Project EPIONE

9

has now gained a lot of maturity and a critical mass of involved scientists to justify an individualized
research axis of the new project Epione, while maintaining many constructive interactions with the 4
other research axes of the project. This will develop all the cardiovascular aspects of the e-patient for
cardiac e-medicine.

The new challenges we want to address in computational cardiology are related to the introduction of
new levels of modeling and to new clinical and biological applications. They also integrate the presence
of new sources of measurements and the potential access to very large multimodal databases of images
and measurements at various spatial and temporal scales.

4 Application domains

The main applications of our research are in the field of healthcare and more precisely the domain of
digital medicine and biomedical data analysis. The axes of research presented above are related to many
branches of medicine including cardiology, oncology, urology, neurology, otology, pneumology, radiology,
surgery, dermatology, nuclear medicine. Within those branches, the applications cover the following
different stages of medicine : prevention, diagnosis, prognosis, treatment.

5 Social and environmental responsibility

5.1 Footprint of research activities

An important activity of Epione is to introduce priors from clinical knowledge within data analysis,
through geometric information, biophysical models, causality, etc. This enables to develop AI method
requiring less data and computations. Therefore with a positive impact on the environmental footprint
of epione research activity.

6 Highlights of the year

6.1 Awards

• The Personalized Computational Cardiology team, including Hervé Delingette and Maxime Ser-

mesant, won the Prix de l’innovation Académie des Sciences - Inria - Dassault Systèmes.

• The PhD thesis of Yann Fraboni was awarded by the Doctoral School EDSTIC of the Premier Prix de

la spécialité ATSI.

7 New software, platforms, open data

7.1 New software

7.1.1 CardiacSegmentationPropagation

Keywords: 3D, Segmentation, Cardiac, MRI, Deep learning

Functional Description: Training of a deep learning model which is used for cardiac segmentation in

short-axis MRI image stacks.

Publication: hal-01753086

Authors: Qiao Zheng, Hervé Delingette, Nicolas Duchateau, Nicholas Ayache

Contact: Qiao Zheng

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Inria Annual Report 2023

7.1.2 CardiacMotionFlow

Keywords: 3D, Deep learning, Cardiac, Classification

Functional Description: Creation of a deep learning model for the motion tracking of the heart, extrac-
tion of characteristic quantities of the movement and shape of the heart to classify a sequence
of cine-MRI cardiac images in terms of the types of pathologies (infarcted heart, dilated , hyper-
trophied, abnormality of the right ventricle).

Publication: hal-01975880

Authors: Qiao Zheng, Hervé Delingette, Nicholas Ayache

Contact: Qiao Zheng

7.1.3 MedINRIA

Keywords: Visualization, DWI, Health, Segmentation, Medical imaging

Scientific Description: MedInria aims at creating an easily extensible platform for the distribution of
research algorithms developed at Inria for medical image processing. This project has been funded
by the D2T (ADT MedInria-NT) in 2010, renewed in 2012. A fast-track ADT was awarded in 2017 to
transition the software core to more recent dependencies and study the possibility of a consortium
creation.The Empenn team leads this Inria national project and participates in the development of
the common core architecture and features of the software as well as in the development of specific
plugins for the team’s algorithm.

Functional Description: medInria is a free software platform dedicated to medical data visualization

and processing.

URL: https://med.inria.fr

Contact: Florent Leray

Participants: Maxime Sermesant, Olivier Commowick

Partners: HARVARD Medical School, IHU - LIRYC, NIH

7.1.4 GP-ProgressionModel

Name: GP progression model

Keywords: Data modeling, Data visualization, Data integration, Machine learning, Biostatistics, Stat-
istical modeling, Medical applications, Evolution, Brain, Uncertainly, Uncertainty quantification,
Alzheimer’s disease, Probability, Stochastic models, Stochastic process, Trajectory Modeling, Marker
selection, Health, Statistic analysis, Statistics, Bayesian estimation

Functional Description: Disease progression modeling (DPM) of Alzheimer’s disease (AD) aims at re-
vealing long term pathological trajectories from short term clinical data. Along with the ability of
providing a data-driven description of the natural evolution of the pathology, DPM has the potential
of representing a valuable clinical instrument for automatic diagnosis, by explicitly describing the
biomarker transition from normal to pathological stages along the disease time axis.

In this software we reformulate DPM within a probabilistic setting to quantify the diagnostic
uncertainty of individual disease severity in an hypothetical clinical scenario, with respect to
missing measurements, biomarkers, and follow-up information. The proposed formulation of
DPM provides a statistical reference for the accurate probabilistic assessment of the pathological
stage of de-novo individuals, and represents a valuable instrument for quantifying the variability
and the diagnostic value of biomarkers across disease stages.

This software is based on the publication:

Project EPIONE

11

Probabilistic disease progression modeling to characterize diagnostic uncertainty: Application to
staging and prediction in Alzheimer’s disease. Marco Lorenzi, Maurizio Filippone, Daniel C. Alexan-
der, Sebastien Ourselin Neuroimage. 2019 Apr 15,190:56-68. doi: 10.1016/j.neuroimage.2017.08.059.
Epub 2017 Oct 24. HAL Id : hal-01617750 https://hal.archives-ouvertes.fr/hal-0161775
0/

Release Contributions: - New interface and output - Completely based on Pytorch

URL: https://gitlab.inria.fr/epione/GP_progression_model_V2

Publication: hal-01617750

Contact: Marco Lorenzi

Participant: Marco Lorenzi

7.1.5 Music

Name: Multi-modality Platform for Specific Imaging in Cardiology

Keywords: Medical imaging, Cardiac Electrophysiology, Computer-assisted surgery, Cardiac, Health

Functional Description: MUSIC is a software developed by the Asclepios research project in close collab-
oration with the IHU LIRYC in order to propose functionalities dedicated to cardiac interventional
planning and guidance. This includes specific tools (algorithms of segmentation, registration, etc.)
as well as pipelines. The software is based on the MedInria platform.

URL: https://team.inria.fr/asclepios/software/music/

Contact: Maxime Sermesant

Participants: Florent Collot, Mathilde Merle, Maxime Sermesant

Partner: IHU- Bordeau

7.1.6 SOFA

Name: Simulation Open Framework Architecture

Keywords: Real time, Multi-physics simulation, Medical applications

Functional Description: SOFA is an Open Source framework primarily targeted at real-time simulation,
with an emphasis on medical simulation. It is mostly intended for the research community to
help develop new algorithms, but can also be used as an efficient prototyping tool. Based on an
advanced software architecture, it allows the creation of complex and evolving simulations by
combining new algorithms with algorithms already included in SOFA, the modification of most
parameters of the simulation (deformable behavior, surface representation, solver, constraints,
collision algorithm etc.) by simply editing an XML file, the building of complex models from simpler
ones using a scene-graph description, the efficient simulation of the dynamics of interacting objects
using abstract equation solvers, the reuse and easy comparison of a variety of available methods.

News of the Year: The new version v20.06 has been released including new elements on SoftRobots +
ModelOrderReduction integration, in addition to an improved architecture and lots of cleans and
bugfixes.

URL: http://www.sofa-framework.org

Publication: hal-00681539

Contact: Hugo Talbot

Participants: Christian Duriez, François Faure, Hervé Delingette, Stephane Cotin, Hugo Talbot, Maud

Marchal

Partners: IGG, CRIStAL

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7.1.7 geomstats

Inria Annual Report 2023

Name: Computations and statistics on manifolds with geometric structures

Keywords: Geometry, Statistic analysis

Scientific Description: Geomstats is an open-source Python package for computations and statistics on
manifolds. The package is organized into two main modules: “geometry“ and “learning“.

The module ‘geometry‘ implements concepts in differential geometry, and the module ‘learning‘
implements statistics and learning algorithms for data on manifolds.

The goal is to provide an easily accessible library for learning algorithms on Riemannian manifolds.

Functional Description: GeomStats is a Python package that performs computations on manifolds such
as hyperspheres, hyperbolic spaces, spaces of symmetric positive definite matrices and Lie groups
of transformations. It provides efficient and extensively unit-tested implementations of these
manifolds, together with useful Riemannian metrics and associated Exponential and Logarithm
maps. The corresponding geodesic distances provide a range of intuitive choices of Machine
Learning loss functions. The operations implemented in GeomStats are available with different
computing backends such as numpy, autograd, pytorch, and tensorflow.

Release Contributions: Major refactoring of the library with automated tests.

News of the Year: A workshop was organized at GSI23, presentations about the package were done at
several conferences/workshops (CSE23 (Amsterdam), Math in the Mine (Tende)) and invited sem-
inars (Mox-Polimi (Milano), FEUP/INEGI (Porto)), contributing to the diffusion of the project. A
major refactoring regarding testing was finished (all the tests were rewritten from scratch), strongly
contributing to the increase of the robustness and correctness of the library. Several other smaller
refactorings were done in order to increase the ease of use and development of the implemented/to
implement mathematical concepts (e.g. introduction of the concept of diffeomorphism). New
spaces and metrics were introduced, in particular those related with SPD/full-rank correlation
matrices. A numerics module was designed and implemented, in order to allow quick proto-
typing and benchmarking of numerical methods. The design and implementation of geodesic
metric spaces (which include stratified spaces) was carried on. A new way of sequentially adding
mathematical structure to a given space started being designed.

URL: https://github.com/geomstats/

Publications: hal-03766900, hal-02536154, hal-02908006, hal-03505132, hal-03160677

Contact: Xavier Pennec

Participants: Olivier Bisson, Nicolas Guigui, Xavier Pennec, Yann Thanwerdas, Nina Miolane, Luis

Pereira, Anna Calissano, Elodie Maignant, Nina Miolane, Alice Le Brigant

Partners: University of California Santa Barbara, Université Panthéon-Sorbonne

7.1.8 MC-VAE

Name: Multi Channel Variational Autoencoder

Keywords: Machine learning, Artificial intelligence, Medical applications, Dimensionality reduction,

High Dimensional Data, Unsupervised learning, Heterogeneity

Scientific Description: Interpretable modeling of heterogeneous data channels is essential in medical
applications, for example when jointly analyzing clinical scores and medical images. Variational
Autoencoders (VAE) are powerful generative models that learn representations of complex data.
The flexibility of VAE may come at the expense of lack of interpretability in describing the joint rela-
tionship between heterogeneous data. To tackle this problem, this software extends the variational
framework of VAE to introduce sparsity of the latent representation, as well as interpretability

Project EPIONE

13

when jointly accounting for latent relationships across multiple channels. In the latent space,
this is achieved by constraining the variational distribution of each channel to a common target
prior. Parsimonious latent representations are enforced by variational dropout. Experiments on
synthetic data show that our model correctly identifies the prescribed latent dimensions and data
relationships across multiple testing scenarios. When applied to imaging and clinical data, our
method allows to identify the joint effect of age and pathology in describing clinical condition in a
large scale clinical cohort.

Functional Description: This software implements the work published in the paper "Sparse Multi-
Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data" presented at the
conference ICML 2019 (Long Beach, California, USA).

The software extends classical variational autoencoders by identifying a joint latent code associated
to heterogeneous data represented in different channels. The software is implemented in Python
and is based on Pytorch. It can be applied to any kind of data arrays, and provides functions for
optimization, visualization and writing of the modeling results.

Release Contributions: First release

URL: https://gitlab.inria.fr/epione_ML/mcvae

Contact: Luigi Antelmi

Participants: Luigi Antelmi, Marco Lorenzi, Nicholas Ayache

Partner: CoBteK

7.1.9 SOFA-CardiacReduction

Keywords: Simulation, 3D modeling, Model Order Reduction, Cardiac

Scientific Description: Modification of a finite element deformation model : meshless approach and
frame-based description, reduction in the number of affine degrees of freedom and integration
points.

Functional Description: This SOFA plugin is intented to build a reduced model for deformable solids

(especially cardiac simulations).

Publication: hal-02429678

Contact: Gaetan Desrues

Participants: Gaetan Desrues, Hervé Delingette, Maxime Sermesant

7.1.10 Fed-BioMed

Name: A general software framework for federated learning in healthcare

Keywords: Federated learning, Medical applications, Machine learning, Distributed Applications, Deep

learning

Scientific Description: While data in healthcare is produced in quantities never imagined before, the
feasibility of clinical studies is often hindered by the problem of data access and transfer, espe-
cially regarding privacy concerns. Federated learning allows privacy-preserving data analyses
using decentralized optimization approaches keeping data securely decentralized. There are cur-
rently initiatives providing federated learning frameworks, which are however tailored to specific
hardware and modeling approaches, and do not provide natively a deployable production-ready
environment. To tackle this issue, Fed-BioMed proposes an open-source federated learning fron-
tend framework with application in healthcare. Fed-BioMed framework is based on a general
architecture accommodating for different models and optimization methods

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Inria Annual Report 2023

Functional Description: The project is based on the development of a distributed software architecture,
and the establishment of a server instance from which remote experiments are triggered on the
clients sites. The software is distributed to the client’s sites, allowing to run machine learning
models on the local data. Model parameters are then transmitted to the server for federated
aggregation.

Fed-BioMed finds application in all projects based on the development of learning models for
multi-centric studies.

Release Contributions: Fed-BioMed is based on Python and Pytorch. The software was recently revised

(under an ADT) to adopt the libraries Pygrid and Pysyft.

URL: https://fedbiomed.gitlabpages.inria.fr/

Publication: hal-02966789v1

Authors: Marco Lorenzi, Santiago Smith Silva Rincon, Marc Vesin, Tristan Cabel, Irene Balelli, Andréa

Senacheribbe, Carlos Zubiaga Pena, Jonathan Levy

Contact: Marco Lorenzi

7.1.11 EchoFanArea

Name: delineation of the border of the fan in ultrasound

Keywords: Ultrasound fan area, Deep learning, Statistics, Image processing

Functional Description: This software allows the delimitation of the acquisition cone in ultrasound
imaging and the inpainting of annotations (lines, characters) inside the cone. It allows both the
perfect de-identification of the images but also to standardize the content of the images. It relies
on a parametric probabilistic approach to generate a training dataset with region of interest (ROI)
segmentation masks. This data will then be used to train a deep U-Net network to perform the
same task in a supervised manner, thus considerably reducing the calculation time of the method,
one hundred and sixty times faster. These images are then processed with existing filling methods
to remove annotations present within the signal area.

URL: https://gitlab.inria.fr/hdadoun/pre-process-US

Publication: hal-03127809

Authors: Hind Dadoun, Hervé Delingette, Nicholas Ayache, Anne-Laure Rousseau

Contact: Hind Dadoun

Partner: Nhance

7.1.12 ProMFusion

Keywords: Image segmentation, Data fusion

Functional Description: Code related to the paper "Robust Fusion of Probability Maps" by Benoît
Audelan, Dimitri Hamzaoui, Sarah Montagne, Raphaële Renard-Penna and Hervé Delingette. The
proposed approach allows to fuse probability maps in a robust manner with a spatial regularization
of the consensus.

URL: https://gitlab.inria.fr/epione/promfusion

Publication: hal-02934590

Contact: Benoit Audelan

Participants: Benoit Audelan, Hervé Delingette, Dimitri Hamzaoui

Project EPIONE

7.1.13 SimulAD

15

Name: Disease progression modeling for clinical intervention simulation

Keywords: Data modeling, Clinical analysis, Clinical trial simulator, Alzheimer’s disease, Pytorch, Vari-

ational Autoencoder, Ordinary differential equations

Scientific Description: Recent failures of clinical trials in Alzheimer’s Disease underline the critical
importance of identifying optimal intervention time to maximize cognitive benefit. While several
models of disease progression have been proposed, we still lack quantitative approaches simulating
the effect of treatment strategies on the clinical evolution. In this work, we present a data-driven
method to model dynamical relationships between imaging and clinical biomarkers. Our approach
allows simulating intervention at any stage of the pathology by modulating the progression speed
of the biomarkers, and by subsequently assessing the impact on disease evolution.

Functional Description: A machine-learning framework allowing to simulate the impact of intervention
on the long-term progression of imaging and clinical biomarkers from collections of healthcare
data

Release Contributions: Most recent version available at https://gitlab.inria.fr/epione/simulad/-/tree/master/

Publication: hal-02968724

Contact: Marco Lorenzi

Participants: Clément Abi Nader, Marco Lorenzi, Nicholas Ayache, Philippe Robert

Partner: Université Côte d’Azur (UCA)

7.2 Open data

Hervé Delingette is the referent for the management of research data at the Inria center of Université
Côte d’Azur. He is acting among a national network of referents located in each Inria center who provides
a forum for close dialogue with scientists, to gather needs and expectations, and to provide advice and
disciplinary expertise.

He gave a 1h presentation on open research data management to PhD students during a Master class

on Open Science at Université Côte d’Azur on May 4th.

8 New results

8.1 Medical Image Analysis & Machine Learning

8.1.1 AI-based Real-time Diagnostic Aid for Abdominal Organs in Ultrasound

This work has been supported by the French government, through the 3IA Côte d’Azur Investments in
the Future project managed by the National Research Agency (ANR) with the reference number ANR-19-
P3IA-0002. It has been also supported by the Health Data Hub and the data warehouse of the Paris hospitals.

Keywords Ultrasound; Medical image analysis; Computer vision; Deep learning.

Participants: Hind Dadoun (Correspondant), Hervé Delingette, Nicholas Ayache,

Anne-Laure Rousseau.

This work of the computer-assisted screening of abdominal organs in ultrasound has been performed

in collaboration with the NHance NGO and the Paris hospitals (Dr Anne-Laure Rousseau).

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Inria Annual Report 2023

• We explored self-supervised and semi-supervised learning methods to leverage unlabeled data for

abdominal organ classification in the presence of very few labeled data [14].

• We have also explored the development of joint representation learning from French radiological
reports and ultrasound images to improve the detection of diseased abdominal organs in ultrasound
[32].

8.1.2 Machine Learning for MR-TRUS Prostate Image Fusion

This work has been supported by the French government, through the 3IA Côte d’Azur Investments in the Fu-
ture project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002.

Keywords Multimodal image registration; Image-guided therapy; Medical image analysis; Statistical

learning.

Participants:

Zhijie Fang (Correspondant), Hervé Delingette, Nicholas Ayache,
Eric Gaudard (Koelis), Paul Wiegel (Koelis).

This is a work performed in collaboration with the Koelis Company (E. Gaudard and P. Wegiel).

• Development of an algorithm to detect anatomical landmarks to initialize the registration of

prostate Ultra Sounds (US) and Magnetic Resonance (MR) images [40].

8.1.3 Prostate and Humerus Shape Analysis

Keywords Medical imaging; Shape analysis.

Participants:

Samy Ferrat (Correspondant), Herve Delingette, Marc-Olivier Gauci.

During the Master internship of Samy Ferrat, and within a collaboration with University Hospital of
Nice (Pr M-O. Gauci) and the Paris hospitals (Pr R. Penna, Hospital Pitié Salpétrière), we have studied the
variability of anatomical shapes (Figure 4) :

• We have evaluated the impact of the shape representations (binary or intensity masks, clamped
distance maps) on the creation of latent representations of the shapes of organs such as prostate or
humerus.

• We have tested both linear (PCA or probabilistic PCA) or non-linear (Variational Auto-encoders)

latent representation models.

• We have developed a processing pipeline for humerus segmentation from CT scan images.

8.1.4 Automatic Segmentation of the Vascular System to Enhance AI-based Decision Support System

for Peripheral Artery Disease

This work was funded by 3IA Côte d’Azur.

Keywords Segmentation; Computed Tomography Angiography (CTA); Vascular system; Peripheral

artery disease.

Project EPIONE

17

(a)

(b)

Figure 4: (a) Prostate Reconstruction from a latent representation of a variational encoder. (b) Segmented
humerus from CT image.

Participants:

Lisa Guzzi (Correspondant), Hervé Delingette, Juliette Raffort-Lareyre,
Maria A. Zuluaga.

The aim of this works is to address the underdiagnosis and difficulty in surgery intervention planning
of Peripheral Artery Disease (PAD) using Computed Tomography Angiography (CTA). Leveraging AI, the
goal is to develop tools for:

• Automatic artery segmentation

• Anatomical and disease characterization to guide diagnosis

• Severity quantification

as illustrated in Figure 5. A review paper [21] focusing on AI’s potential for vascular clinical decision-
making was also produced.

Figure 5: Overview of the project on automatic segmentation of CT images for peripheral artery diseases

8.1.5 AI-Based Diagnosis of Prostate Cancer from Multiparametric MRI

This work has been supported by funding from the 3IA Côte d’Azur Investments, UniCA DS4H graduate
school, by the Health Data Center of the AP-HP (Assistance Publique-Hôpitaux de Paris) and by the Alain

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Inria Annual Report 2023

Rahmouni Grant from the French Radiology Society-CERF

Keywords Prostate; Segmentation; Consensus; Inter-rater variability.

Participants: Dimitri Hamzaoui (Correspondant), Sarah Montagne, Sebastien Mo-

liere, Raphaële Renard-Penna, Nicholas Ayache, Herve Delingette.

The aim of this work is to study the impact of inter-rater variability on prostate segmentation, in order

to improve prostate cancer detection. To do so, we mainly worked on two projects:

• First, we developed a new method to produce binary and probabilistic segmentation consensuses
from binary segmentations (see Figure 6 (a)). The method is based on an iterative optimization of
the Frechet Variance for specific metrics. In particular, the choice of overlap-based metrics allow us
to obtain a background-independent result [17].

• Second, we made an extensive study on how the number of raters can impact the inter-rater
variability on prostate segmentation (see Figure 6 (b)). This study was made with seven radiologists
with various levels of experience. We observed that using 3 segmentations there was a tipping point
in variability for both pairwise and consensus comparisons. Therefore we retained 3 segmentations
as an appropriate number for such comparisons [22].

These works have been published in the PhD manuscript of Dimitri Hamzaoui [48].

(a)

(b)

Figure 6: (a) Principle of the new segmentation consensus method. (b) Methodology of the inter-rater
variability study.

8.1.6 Thyrosonics

This project has received funding through BoostUrCAreer from the European Union’s Horizon 2020 re-
search and innovation program under grant agreement Nr. 847581. It has been co-funded by the Region

Project EPIONE

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Provence-Alpes-Côte d’Azur and IDEX UCAJEDI.

Keywords Thyroid cancer; Ultrasound.

Participants: Hari Sreedhar (Correspondant), Hervé Delingette, Guillaume Lajoinie,

Charles Raffaelli.

• This project investigates thyroid ultrasound machine learning applications in the context of an-

notation difficulties and the physics of nonlinear wave propagation.

• A weighted active learning selection strategy (Figure 7) was presented as a poster at the 2023
MICCAI Workshop on Data Augmentation, Labeling, and Imperfections [36]. It introduces a rigged
draw strategy for active learning which is an uncertainty-weighted selection approach suited for
supervised and semi-supervised learning.

• Elements of the work were presented at the 2023 Atelier Thyroïde de Sète (May 27) and at the Club

Thyroïde Côte d’Azur (Nov 28)

Figure 7: Active learning cycle from [36] on left, with the weighted rigged draw strategy on the right.

8.1.7 AI-based Selection of Imaging and Biological Markers Predictive of Treatment Response in

Lung Cancer

This work has been supported by the French government, through the 3IA Côte d’Azur Investments in the Fu-
ture project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002.

Keywords Digital pathology; Deep learning; Multiple instance learning; Mixed supervision.

Participants:

Paul Tourniaire (Correspondant), Hervé Delingette, Nicholas Ayache,
Marius Ilié, Paul Hofman.

• Thesis defended: AI-based selection of imaging and biological markers predictive of therapy

response in lung cancer [54].

• Development of MS-CLAM, an algorithm for the use of mixed supervision for the improvement of

classification and localization of tumors with few labels [26].

• Histology-based survival prediction to select patients for an immunotherapy treatment for lung

cancer patients (Figure 8).

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Inria Annual Report 2023

Figure 8: The workflow developed to predict post-immunotherapy survival among patients suffering
from lung cancer, using pathology slides.

8.2 Imaging & Phenomics, Biostatistics

8.2.1 Development of Genome-to-Phenome Association Methods Using Biologically Inspired Con-

straints

This work was funded by the grant ANR PARIS-15087 and ANR MITOMICS 2022000174.

Keywords Bayesian Variational dropout; Mitochondrial genome; Phenome; Regression; Biological

constraint; Variant co-occurence.

Participants: Marie Deprez (Correspondant), Marco Lorenzi.

In the study of rare mitochondrial diseases, many analysis have been performed on mitochondrial
DNA to identify rare variants and evaluate their pathogenicity. Yet, only a small number of studies focus
on the co-occurences between multiple mitoDNA variants and/or nuclear variants and their impact on
the pathological traits. Our participation in the MITOMICS project extends our previous work on the joint
analysis of genomic and phenotypic data to uncover the genetics underpinning complex pathological
traits. This project is developed following multiple steps :

• Understanding the biological differences between mitochondrial and nuclear DNA (see Figure 9).
A Mitochondrial DNA (mtDNA) is circular, present in multiple copies and its maintenance is
controlled both by itself and nuclear DNA.

• Study the biological / functional constraints that can be used for the prioritization of mtDNA
variants, Figure 9.B. Including variants in protein-coding genes, but also in transfer RNAs (tRNAs)
and ribosomal RNAs (rRNAs).

• Some administrative work was also required to approve data security and management due to the

specific sensibility of genomic data.

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Figure 9: (A) Graphical representation of human mitochondrial variations. The outer circle depicts the
mitochondrial genome with annotated tRNAs (gray), rRNAs (purple), protein-coding genes and non-
coding regions (white). In the inner circle each point represents an mtDNA variant reported in Genbank
collected from Mitomap according to the reported variant status (polymorphisms in green, reported
pathogenic in orange and confirmed pathogenic in red) and frequency. (B) Mitochondrial DNA encodes
13 integral subunits of the electron transport chain. Figure adapted from Jones et al. (2021).

8.2.2 Exploring Latent Dynamical Models for the Analysis of High-dimensional and Heterogeneous

Data

This work was funded by 3IA Côte d’Azur.

Keywords Density estimation; Neural ODEs; Diffusion probabilistic models; Distribution tails; Anom-

aly detection.

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Inria Annual Report 2023

Participants:

Etrit Haxholli (Correspondent), Marco Lorenzi.

Density estimation is the statistical process of constructing a probabilistic model that represents the

distribution of a given dataset. Our contributions to the field of density estimation include:

• We defined a novel theoretical framework for the robust estimation of the tails of distributions in

complex setting.

• We exploit the property of diffusion probabilistic models wherein scores describing the diffusion
process can be optimized independently from each other [42]. We demonstrate that exploiting this
property can substantially reduce training time and enhance density estimation performance, as
shown in Figure 10.

• Defended and published the PhD thesis of Etrit Haxholli entitled "Scalable and Flexible Density

Estimation for Complex Data Distributions" [49].

Figure 10: Probability density modeling capabilities as a function of the number of groups of scores
learned independently which increases from left to right.

8.3 Computational Anatomy & Geometric Statistics

8.3.1 Riemannian and Sub-riemannian Methods for Dimension Reduction

This work was funded by the ERC grant #786854 G-Statistics from the European Research Council under
the European Union’s Horizon 2020 research and innovation program.

Keywords Differential geometry; Dimension reduction; Manifold learning.

Participants: Morten Akhoj Pedersen (Correspondant), Xavier Pennec, James Benn,

Stefan Sommer (UCPH, DK).

The PhD thesis of Morten Akhoj Pedersen [52] proposes two new methods for dimension reduction

based on differential geometry.

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1. We generalize a method used in evolutionary biology, phylogenetic principal component analysis,

to data on a Riemannian manifold, e.g. shape data. This has been published in [28].

2. We construct a non-parametric method, Principal subbundles, that can be used for approximating a
set of observations, in either Euclidean space or a Riemannian manifold, by a flexible submanifold -
see Fig. 11. The preprint [57] provides more details.

Figure 11: Top: noisy observations on the S-surface, embedded in R100 but projected to R3 for the purpose
of visualization. The turquoise surface shows our 2-dimensional manifold reconstruction, using the
method of Principal Subbundles. Below: a 2D tangent space representation of the observations.

8.3.2 Towards a BCH Formula on the Diffeomorphism Group with a Right-invariant Metric

This work was funded by the ERC grant #786854 G-Statistics from the European Research Council under
the European Union’s Horizon 2020 research and innovation program.

Keywords Lie groups; Diffeomorphism groups; Baker-Campbell-Hausdorff; Right-invariant geometry.

Participants:

James Benn (Correspondant), Stephen Marsland (Victoria Uni. of
Wellington, New Zeland).

The Baker-Campbell-Hausdorff (BCH) formula links a Lie group with its Lie algebra by giving a formula
for group multiplication of elements close to the identity in terms of the Lie bracket. This has many
practical applications to geometry, algebra, and partial differential equations, and provides simple proofs
of further developments of Lie theory. To date, there is no BCH formula for the diffeomorphism groups -
infinite dimensional Lie groups which naturally arise in continuum mechanics and diffeomorphometry
for medical imaging (such as Large Deformations Diffeomorphic Metric Mapping - LDDMM or Stationary
Velocity Fields - SVF). In the paper "Towards a BCH Formula on the Diffemorphism Group with a Right-
Invariant Metric" [59] we provide the relevant background in an expository manner, starting with finite
dimensional Lie Groups, and take a first step towards developing a BCH formula for the exponential map
of a right-invariant metric on a Lie Group G by deriving an explicit formula for its derivative, in the spirit
of Poincaré. We then identify which modifications are necessary for the finite dimensional results to lift
to the infinite dimensional setting of the Diffeomorphism groups and re-prove our finite dimensional
results on the diffeomorphism groups. Along the way we discuss additional applications of our formulas
and conclude with a program to complete the BCH formula.

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Inria Annual Report 2023

8.3.3 Graph Alignment Exploiting the Spatial Organization Improves the Similarity of Brain Networks

This work was funded by the ERC grant #786854 G-Statistics from the European Research Council under
the European Union’s Horizon 2020 research and innovation program.

Keywords Network valued data; Graph space; Brain connectivity; Graph matching.

Participants:

Anna Calissano (Correspondant), Samuel Deslauriers-Gauthier,
Theodore Papadopoulo, Xavier Pennec.

Can the structural and functional properties of a specific brain region of an atlas be assumed to be the
same across subjects? We addressed this question by looking at the network representation of the brain,
with nodes corresponding to brain regions and edges to their structural relationships [10]. We perform
graph matching on a set of control patients and on parcellations of different granularity to understand
which is the connectivity misalignment between regions. The graph matching is unsupervised and reveals
interesting insight on local misalignment of brain regions across subjects as shown in Figure 12.

Figure 12: Surface visualization of the permutation type. For each region, we considered the rate at which
a region was permuted with any other region (i.e. not a self permutation), with a region that is not a 1st
degree neighbor, and with a region that is not a 1st or 2nd degree neighbor. Warmer and colder colors
indicate a higher and lower permutation rates, respectively. Gray indicates no permutation. The three
rows correspond to a brain parcellation with 100, 300, and 1000 regions.

8.3.4 Towards Barycentric Subspace Analysis for Graphs

This work was funded by the ERC grant Nr. 786854 G-Statistics from the European Research Council under
the European Union’s Horizon 2020 research and innovation program.

Keywords Network valued data; Graph space; Barycentric subspace analysis; Spectral graph theory.

Project EPIONE

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Participants:

Anna Calissano (Correspondant), Elodie Maignant, Xavier Pennec.

Barycentric Subspaces have been defined in the context of manifolds using the notion of exponential
barycenters. In this work, we extend the definition to quotient spaces which are not necessary manifolds.
We define an alignment map and an horizontal logarithmic map to introduce Quotient Barycentric
Subspaces (QBS). Due to the discrete group action and the quotient structure, the characterization of
the subspaces and the estimation of the projection of a point onto the subspace is far from trivial. We
propose two algorithms towards the estimation of the QBS and we discussed the results, underling the
possible next steps for a robust estimation and their application to different data types [30].

8.3.5 Riemannian Geometry and Geometric Statistics: from Basic Theory to Implementation with

Geomstats

This work was supported by the ERC grant #786854 G-Statistics from the European Research Council
under the European Union’s Horizon 2020 research and innovation program and by the French government
through the 3IA Côte d’Azur Investments ANR-19-P3IA-0002 managed by the National Research Agency.

Keywords Riemannian geometry; Geometric statistics; Geomstats software.

Participants: Nicolas Guigui, Xavier Pennec (Correspondant), Nina Miolane (Univ.

California Santa Barbara, USA).

Riemannian geometry is a natural framework to model and unify complex nonlinear sources of data.
However, the development of computational tools from the basic theory of Riemannian geometry is
laborious. The work presented in [16] forms one of the main contributions to the open-source project
geomstats, that consists in a Python package providing efficient implementations of the concepts of
Riemannian geometry and geometric statistics, both for mathematicians and for applied scientists for
whom most of the difficulties are hidden under high-level functions. The goal of this monograph is two-
fold. First, we aim at giving a self-contained exposition of the basic concepts of Riemannian geometry,
providing illustrations and examples at each step and adopting a computational point of view. The
second goal is to demonstrate how these concepts are implemented in Geomstats, explaining the choices
that were made and the conventions chosen. The general concepts are exposed and specific examples
are detailed along the text. The culmination of this implementation is to be able to perform statistics and
machine learning on manifolds, with as few lines of codes as in the wide-spread machine learning tool
scikit-learn. We exemplify this with an introduction to geometric statistics (Figure 13).

8.3.6 Geometry of Sample Spaces

This work was partly supported by the ERC grant #786854 G-Statistics from the European Research Council
under the European Union’s Horizon 2020 research and innovation program and by the French government
through the 3IA Côte d’Azur Investments ANR-19-P3IA-0002 managed by the National Research Agency.

Keywords Riemannian geometry; Geometric statistics.

Participants:

Philipp Harms (Nanyang Technological University, Singapour),
Peter Michor (University of Vienna , Austria), Xavier Pennec (Corres-
pondant), Stefan Sommer (Univ. Copenhagen, DK).

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Inria Annual Report 2023

Figure 13: Visualization of geodesics and one-parameter subgroups on SE(2).

In statistics, independent, identically distributed random samples do not carry a natural ordering,
and their statistics are typically invariant with respect to permutations of their order. Thus, an n-sample
in a space M can be considered as an element of the quotient space of Mn modulo the permutation group.
We take in [18] this definition of sample space and the related concept of orbit types as a starting point for
developing a geometric perspective on statistics. We aim at deriving a general mathematical setting for
studying the behavior of empirical and population means in spaces ranging from smooth Riemannian
manifolds to general stratified spaces. We fully describe the orbifold and path-metric structure of the
sample space when M is a manifold or path-metric space, respectively. These results are non-trivial even
when M is Euclidean. We show that the infinite sample space exists in a Gromov-Hausdorff type sense
and coincides with the Wasserstein space of probability distributions on M. We exhibit Fréchet means
and k-means as metric projections onto 1-skeleta or k-skeleta in Wasserstein space, and we define a new
and more general notion of polymeans. This geometric characterization via metric projections applies
equally to sample and population means, and we use it to establish asymptotic properties of polymeans
such as consistency and asymptotic normality.

8.3.7 Barycentric Subspaces for Geometric Learning

This work was funded by the ERC AdG G-Statistics (#786854) from the European Research Council under
the European Union’s Horizon 2020 research and innovation program.

Keywords Non-Euclidean data analysis; Manifold learning; Statistical graph analysis.

Participants:

Elodie Maignant (Correspondant), Xavier Pennec, Alain Trouvé (Centre
Borelli, ENS Paris-Saclay), Anna Calissano.

In the PhD of Elodie Maigant [51], we explore further novel dimensionality reduction methods for
the analysis of non-Euclidean data based on the notion of barycentric subspace. This includes more
specifically:

• the generalization of Locally Linear Embedding (LLE) to non-Euclidean data [34] and the geometric

characterization of such embedding;

• the construction of a new framework for statistical graph analysis as well as the implementation of

barycentric susbpace analysis within this framework (Figure 14).

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Figure 14: Barycentric subspace analysis of a set of graphs.

8.3.8 Riemannian Logarithm on Flag Manifolds

This work was supported by the ERC grant Nr. 786854 G-Statistics from the European Research Council
under the European Union’s Horizon 2020 research and innovation program and by the French government
through the 3IA Côte d’Azur Investments ANR-19-P3IA-0002 managed by the National Research Agency.

Keywords Flag manifolds; Riemannian logarithm; Orthogonal alignment; Procrustes analysis.

Participants:

Tom Szwagier (Correspondant), Xavier Pennec.

In [37], we propose an algorithm to approximate the Riemannian logarithm and geodesic distance on

flag manifolds.

• We formulate their computation as an optimization problem on orthogonal matrices.

• We relax this problem and find an explicit solution related to Procrustes analysis.

• The associated iterative algorithm (cf. Figure 15) brings drastic improvements over the state-of-the-

art and has been made available on GitHub.

8.3.9 Riemannian and Stratified Geometries on Covariance and Correlation Matrices

This work was partly supported by the ERC grant #786854 G-Statistics from the European Research Council
under the European Union’s Horizon 2020 research and innovation program and by the French government
through the 3IA Côte d’Azur Investments ANR-19-P3IA-0002 managed by the National Research Agency.

Keywords Riemannian geometry; Geometric statistics ; SPD matrices ; Correlation matrices ; Bures-

Wasserstein geodesics.

Participants:

Yann Thanwerdas (correspondant), Xavier Pennec.

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Inria Annual Report 2023

Figure 15: Illustration of the proposed alignment algorithm to approximate the Riemannian logarithm
and geodesic distance on flag manifolds.

We investigated families of Riemannian and stratified geometries on covariance and correlation
matrices. For SPD matrices, we characterized all the continuous metrics invariant by O(n) by means of
three multivariate continuous functions, that encompass the classical metrics (log-Euclidean, Bogoliubov-
Kubo-Mori - BKM, power-Euclidean) for which we detail all the basic Riemannian operations [25] (see
Figure 16). A side result was the characterization of invariant inner products [38]. On the space of full-rank
correlation matrices, we showed that the curvature of the recently introduced quotient-affine metric is of
non-constant sign and unbounded from above. We introduce computationally more convenient Hadam-
ard or even log-Euclidean metrics, along with their geometric operations. To recover the lost invariance
under permutations, we define two new permutation-invariant log-Euclidean metrics, one of them
being invariant under a natural involution on full-rank correlation matrices. For positive semi-definite
symmetric (PSD) matrices, we studied the stratified Riemannian structure of the Bures-Wasserstein
distance. A notable contribution was the computation of the injection domain within each stratum and
the characterization of the length-minimizing curves between the strata of different ranks [24].

8.4 Computational Cardiology & Image-Based Cardiac Interventions

8.4.1 Causal Data Analysis for Torsade de Pointes Risk Assessment

This work was supported by the European Union’s Horizon 2020 Research and Innovation Program Sim-
CardioTest project which has received funding from the European Union’s Horizon 2020 research and
innovation program under grant agreement No. 101016496.

Keywords Causal discovery; Bayesian approach; Biomarker discovery; Variational auto-encoder;

In-silico trials.

Participants:

Safaa Al-Ali (Correspondant), Irene Balelli, Maxime Sermesant,
Jordi Llopis Lorente (Centro de Investigación e Innovación en Bioin-
geniería (Ci2B), Universitat Politecnica de Valencia, Valencia, Spain),
Maria Teresa Mora (Ci2B), Beatriz Trenor (Ci2B).

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Figure 16: Interpolation between two SPD matrices with different Riemannian metrics.

• In [29], we employed the causal discovery method ICA-Linear Non-Gaussian Acyclic Model (ICA-
LiNGAM) to uncover the relations across the 7 ion channels identified by the Comprehensive in
vitro Pro-arrhythmia Assay (CiPA) initiative as potentially related to the induction of Torsade de
Pointes (TdP) risk. We identified I_Kr , I_NaL and I_CaL as the ones that directly affect TdP-risk
assessment, and suggested that I_Na perturbations could potentially impact on pro-arrhythmic
risk induction (cf Figure 17 (1-3)). Furthermore, we independently performed binary drug risk
classification and showed that the combination of the 3 selected ionic currents maximizes the
classification accuracy outperforming state-of-the-art approaches based on alternative ion channel
combinations.

• Each drug used in [29] has an associated known Effective Therapeutic Plasma Concentration
(EFTPC), which denotes the concentration of the drug in the bloodstream required to produce the
desired therapeutic effects. We simulated a variation of the drug plasma concentration, searching
for a threshold above which each molecule turns out to be unsafe. We found that a concentration of
approximately six times the EFTPC is sufficient to classify all intermediate classes’ drugs as unsafe
(Figure 17).

8.4.2 Efficient Patient-Specific Simulations of Ventricular Tachycardia Based on Computed Tomography-

defined Wall Thickness Heterogeneity

This work was funded by ANR-10-IAHU-04 "Investissements du futur".

Keywords Cardiac modeling; Electrophysiology modeling; Computed tomography; Ventricular tachy-

cardia.

Participants: Nicolas Cedilnik (Correspondant), Mihaela Pop, Maxime Sermesant.

Time tt=0t=1Eucl.E(2)xQAE(1)xQALExQAE(-1)xQAAff-inv.Log-Eucl.30

Inria Annual Report 2023

Figure 17: Pipeline of the proposed method inspired by [29]. (1) Data preparation: computation of ionic
currents blockade for class 1 and class 4 drugs. (2) Application of the ICA-LINGAM algorithm to define the
causal cascade leading to the drug safety labelling, and (3) Selection of the most relevant ionic currents
for proarrhythmic risk assessment. (4) Training of the binary classifier on classes 1 and 4 drugs, and
validation on the intermediate risk classes i.e. class 2 and class 3 drugs, using the retained combination
of ionic currents. (5) Simulation of the safety threshold for plasma drug concentration for each molecule
in the intermediate risk classes.

• Foundational work has shown that acquiring heart-specific electrophysiological data allows model

personalization able to simulate ischemic ventricular tachychardia re-entrant patterns.

• Later work has shown that CT-defined left ventricular wall thickness is correlated to electrophysiolo-

gical properties and that this imaging feature is suitable for model personalization.

• Our latest work [11] pushes the concept further by reproducing the VT induction procedure using

reaction-diffusion models parametrized on CT-defined wall thickness (Figure 18).

Figure 18: Flowchart of 3-Dimensional Computed Tomography–Based Biventricular Model Construction

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8.4.3 Personalization of Cardiac Models for the Prediction of Cardiac Resynchronization Therapy

Response

This work has been supported by the French government through the National Research Agency (ANR)
Investments in the Future 3IA Côte d’Azur (ANR-19-P3IA-000) and by Microport CRM funding.

Keywords Patient-specific; Digital twin; ECG; Biomechanics; Finite elements method; Cardiac elec-

tromechanical model.

Participants: Gaëtan Desrues (Correspondant), Serge Cazeau (Microport CRM), Thi-
erry Legay (Microport CRM), Delphine Feuerstein (Microport CRM),
Maxime Sermesant.

Patient-specific 3D electromechanical models can help in improving patient selection, therapy op-
timization and interventional guidance. The aim of this project is to study the response to cardiac
resynchronization therapy, with the help of personalized cardiac models, see Figure 19. The following
work has been realized:

• Calibration of the active and passive mechanical properties of cardiac tissue,

• AI-based personalization of the electromechanical model using cardiac indices measured on both

healthy and diseased patients,

• Predictions to CRT response through pacing simulations,

• Preparation and publication of the PhD manuscript of G. Desrues [46].

Figure 19: Global pipeline for patient-specific simulations and prediction to CRT response.

8.4.4 Cardiac Electrophysiology Model Correction Using Differentiable Physics and Deep Learning

This PhD has been funded by 3IA Côte d’Azur DeepNum project (ANR-21-CE23-0017)

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Inria Annual Report 2023

Keywords Physics-based learning; Deep learning; Cardiac electrophysiology; Simulations.

Participants:

Victoriya Kashtanova (Correspondant), Mihaela Pop, Maxime Sermes-
ant, Ibrahim Ayed (LIP6, Paris), Patrick Gallinari (LIP6, Paris).

We present a fast hybrid approach to learn cardiac electrophysiology dynamics from data. Our novel
framework has two components, decomposing the dynamics into a physics and a data-driven term (see
Figure 20). We show experimentally in [20, 50] that this construction allows our framework to learn from
data of different complexity, while simultaneously estimating model parameters. This physics-based
data-driven approach may improve cardiac EP modeling by providing a robust biophysical tool for
predictions.

Figure 20: General APHYN-EP framework. Correction of a PDE-based model (Fp ) with an additional term
(Fd ) learned from the data

8.4.5 Outcome Prediction in Cardiology: a Practical Guide

Part of this work has been supported by the French Government, through the National Research Agency
(ANR) 3IA Côte d’Azur (ANR-19-P3IA-0002), IHU Liryc (ANR- 10-IAHU-04), Université Côte d’Azur and
Ecole Doctorale Sciences et Technologies de l’Information et de la Communication (EDSTIC).

Keywords Outcome prediction; AI; Cardiology; Explainable AI.

Participants: Buntheng Ly (Correspondant), Mihaela Pop, Hubert Cochet, Nic-
olas Duchateau, Declan O’regan, Maxime Sermesant.

We contributed to the Outcome Prediction chapter, as part of the AI and Big Data Application in
Cardiology: A Practical Guide textbook [45]. The chapter covered the state-of-the-art methodologies
in three clinical applications: cardiac resynchronization therapy (CRT) response, atrial fibrillation and
ventricular arrhythmia (Figure 21).

8.4.6 Inducibility of Ventricular Tachycardia by Means of Different Stimulation Protocols: an Electro-

physiology Simulation Study

This work has received funding from the European Union’s Horizon 2020 research and innovation pro-

Project EPIONE

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Figure 21: Example of a generic pipeline for building AI-based models to predict survival rate or therapy
outcome for cardiac application.

gramme under grant agreement No 101016496 (SimCardioTest), and from the ANR’s PEPR project ChroniC-
ardio, contract number 2023000289.

Keywords Cardiac modeling; Electromechanical activity; Personalization; Simulation; Finite element.

Participants:

Jesus Jairo Rodríguez Padilla (Correspondant), Buntheng Ly, Ra-
fael Silva, Mihaela Pop, Maxime Sermesant.

In this work we propose to leverage on computer simulations to study the inducibility of ventricular
tachycardia (VT) by replicating the stimulation protocols typically employed in the clinics, but testing
different locations of the stimulus site. This will allow us to gain insight into the effect of stimulus location
on the electrophysiologic response in the presence of scar tissue. Figure 22 shows the whole pipeline
of our study as well as two simulation results showing different electrical behaviors depending on the
pacing site location.

8.4.7 Unsupervised Polyaffine Transformation Learning for Echocardiography Motion Estimation

This work has been funded by 3IA Côte d’Azur (ANR-19-P3IA-0002) and Inria.

Keywords Echocardiography; Motion tracking.

Participants:

Yingyu Yang (Correspondant), Pamela Moceri, Marie Rocher,
Maxime Sermesant.

The main contributions in this project include [39]:

• the development of a novel polyaffine motion model (PAM) for unsupervised cardiac motion

estimation;

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Inria Annual Report 2023

Figure 22: Pipeline process to generate realistic electrophysiological simulations: (1) MR images, cardiac
structure (DTI-based atlas), electrophysiology (CARTO); (2) Segmentation and mesh generation; (3)
Personalization and Calibration; Simulation results.

• the design of a comprehensive pipeline for using PAM in a weakly-supervised manner on a large

public echocardiography dataset (Figure 23);

• comprehensive experiments on various real-world datasets, showing that PAM outperforms other
popular deep learning-based motion estimation methods and thereby highlighting its potential for
clinical applications.

This work has been included in the PhD manuscript of Yingyu Yang [55].

8.5 Multi-centric data and Federated Learning

8.5.1 Federated Missing Data Imputation: Towards Federated Pre-processing

This work was supported by the French government, through the 3IA Côte d’Azur Investments in the Fu-
ture project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002.

Keywords Federated learning; Federated pre-processing; Missing data; Variational autoencoders;

Deep learning; Multiple imputation.

Participants:

Irene Balelli (Correspondant), Aude Sportisse (Post Doc, MAASAI Team),
Francesco Cremonesi, Pierre-Alexandre Mattei (CR, MAASAI Team),
Marco Lorenzi.

Despite Federated Learning (FL) has shown great potential for cross-institutional medical research,
a major bottleneck for its real-world deployment is how to carry out data pre-processing, form simple
operations such as data standardization, up to the management of missing entries in the databases.

• We propose Fed-MIWAE [58], a federated version of the state-of-the-art imputation method MIWAE,
a deep latent variable model for imputation of Missing At Random (MAR) data based on variational
autoencoders.

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Figure 23: Method overview. Given a sequence of input images, an encoder-decoder network first extracts
K key points and the corresponding local affine transformation. Second, by applying polyaffine motion
fusion, the final dense motion field is obtained.

• We evaluate our method on multi-modal medical imaging data and clinical scores from a simulated
federated scenario with the ADNI dataset and on the Traumabase dataset, a multicentric cohort
study dedicated to the management of polytrauma patients.

• We compare Fed-MIWAE with respect to classical imputation methods, either performed locally or
in a centralized fashion (Fig. 24 (a)), achieving with Fed-MIWAE an imputation accuracy compar-
able with the centralized training, even when local data distributions are highly heterogeneous,
and the local dataset size is small.

• The variational nature of Fed-MIWAE makes it naturally designed to perform multiple imputation
(Fig. 24 (b)), allowing for the quantification of the imputation uncertainty in the federated scenario.

8.5.2 Real-world Deployment of Federated Learning in Biomedical Research Consortia with Fed-

BioMed

This project received financial support by the French government through the Agence Nationale de la
Recherche (ANR), project Fed-BioMed ref. num. ANR-19-CE45-0006.

Keywords Biomedical data; Federated learning; Machine learning.

Participants:

Francesco Cremonesi (Correspondant), Sergen Cansiz, Yannick Bouil-
lard, Marc Vesin, Irene Balelli, Marco Lorenzi.

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Inria Annual Report 2023

Figure 24: (a) Comparison of federated and local models on single imputation, in all local datasets
and in the external one, measured through MSE, for the ADNI (left) and Traumabase (right) datasets.
(b) An example of multiple imputation on one patient of the ADNI dataset. The left panel shows the
corresponding comparisons between pFed-MIWAE(xmissing|xobserved) with p(xmissing).

Fed-BioMed is an open-source initiative aimed at enabling real-world deployment of federated

learning in biomedical research [63].

Fed-BioMed has been successfully deployed in hospital setting, and will be scaled out to larger federa-
tions in the coming year. In the first instance, three French hospitals from the UniCancer consortium
(Centre Antoine Lacassagne, Centre Henri Becquerel, Institut Curie) have participated in the joint training
of a prostate segmentation task using publicly available data. Fed-BioMed was deployed in all three
hospitals under real-world conditions, and a successful training of the model was achieved leading to a
performance comparable with a centralized approach, as shown in Figure 25. In the second instance,
Fed-BioMed was deployed in two nodes of the EUCAIM project network, under real-world conditions, as
part of the M9 demonstrator milestone, to achieve a proof-of-concept training of both a logistic regression
and an image classification task using public data.

8.5.3 Reliability and Robustness of Federated Learning in Practical Applications

This work was supported by a CIFRE contract with Accenture.

Keywords Federated learning; Machine learning; Data heterogeneity; Optimization; Machine un-

learning.

Participants:

Yann Fraboni (Correspondant), Richard Vidal (Accenture), Laeti-
tia Kameni (Accenture), Sebastien Ourselin (King’s College London),
Marco Lorenzi.

Federated learning (FL) enables a set of participants to jointly learn a global model without sharing
their data. To tackle the problem of Federated Optimization in heterogeneous and sensitive setting, we
proposed the following contributions:

• We introduced a novel theoretical framework to account for asynchronous FedAvg, where the server

creates a new global model whenever receiving a client’s contribution [15].

<latexit sha1_base64="YQ/tnky5ATteE+wh4wmQvkcldc4=">AAACz3icjVHLTsJAFD3UF+ILdemmkZjghhRD1CXRjUtIBEyAmHYYsKGvTKcqIRi3/oBb/SvjH+hfeGcsiUqMTtP2zLn3nJl7rxN5biwt6zVjzM0vLC5ll3Mrq2vrG/nNrWYcJoLxBgu9UFw4dsw9N+AN6UqPX0SC277j8ZYzPFXx1jUXsRsG53IU8a5vDwK37zJbEtXpSH4rhT8u2vuTy3zBKll6mbOgnIIC0lUL8y/ooIcQDAl8cASQhD3YiOlpowwLEXFdjIkThFwd55ggR9qEsjhl2MQO6TugXTtlA9orz1irGZ3i0StIaWKPNCHlCcLqNFPHE+2s2N+8x9pT3W1Efyf18omVuCL2L9008786VYtEH8e6BpdqijSjqmOpS6K7om5ufqlKkkNEnMI9igvCTCunfTa1Jta1q97aOv6mMxWr9izNTfCubkkDLv8c5yxoHpTKh6VKvVKonqSjzmIHuyjSPI9QxRlqaJB3hEc84dmoGzfGnXH/mWpkUs02vi3j4QMgPZQm</latexit>(a)<latexit sha1_base64="9D3OaBEwVD0rdQMDYS1PVLpCzQI=">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</latexit>(b)Project EPIONE

37

Figure 25: a) Pixel intensity distribution grouped by clinical site. b) Breakdown of FL experiment wallclock
runtime. c) Distribution of Dice scores for centralized (CL) and federated (FL) models, combining all
cross-validation folds. d) Clockwise from top left: an example raw image, the ground truth segmentation,
the FL model prediction and the CL model prediction.

• We develop IFU, a novel unlearning framework for FL, that can measure a client’s contribution in a

trained model and unlearn it upon request with statistical guarantees [64].

• In collaboration with King’s College London, Federated Unlearning with IFU was demonstrated
on a real worl use-case for federated learning of a medical image segmentation model in prostate
cancer analysis [33] (Figure 26).

• The ensemble of contributions were presented in the doctoral dissertation [47].

8.5.4 Multi-centric AI-based Framework for Prostate Cancer Patients Management on Active Surveil-

lance Through Robust Federated Learning

This work has been funded by 3IA Côte d’Azur.

Keywords Active surveillance; Distributed learning; Prostate cancer.

Participants:

Lucia Innocenti (Correspondant), Marco Lorenzi, Michela Antonelli,
Sebastien Ourselin.

Collaborative learning (CL) in healthcare is essential to address data governance-related challenges.
Indeed, CL is emerging as a potent strategy for exploiting collective expertise and data resources from a
federation of clients without the need to share data or sensitive information.

Our study [43] provides a comprehensive examination of CL methods, by benchmarking them in
terms of performance and cost-effectiveness for a specific case study: multi-center prostate segmentation
from 3D MRIs (Figure 27).

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Inria Annual Report 2023

Figure 26: IFU Correctly unlearns a given data instance while guaranteeing accuracy on the remaining
training data. Top: on the data to be unlearnt, the segmentation quality is equivalent to the one of a
model trained from scratch without that data. Bottom: on data to be retained, the segmentation remains
of good quality, at par with the model trained from scratch. We note that IFU outperforms the competing
methods in terms of unlearning quality.

• We compared two CL approaches: Federated Learning (FL) and Consensus-Based Methods (CBM).

• In this specific scenario, CBM and FL are comparable in terms of performance.

• At the same time, CBM is more cost-effective, requiring fewer resources to be trained and naturally

addressing some FL open problems like client dropout.

These insights will benefit researchers and practitioners seeking to optimize collaborative learning in
healthcare, improving patient care and data security.

Figure 27: The table on top shows the comparison of the 5-fold DSC obtained in the segmentation task
by different training strategies. The table in the middle shows the cost comparison of different training
strategies, in terms of training and inference time and communication bandwidth. On the bottom, a
representation of the segmentation task on a sample image using different strategies. In white, the ground
truth; in red, the segmentation provided by each training approach.

Project EPIONE

39

8.5.5 StratifyAging: Interoperatibility of Clinical Studies and Routine Care for the Advent of a Strati-

fied Medicine of Aging Using Clinical, Imaging, and Omics Data

This project received financial support by the PEPR Santé Numerique.

Keywords Federated learning; MRI image analysis, Neuroimaging studies.

Participants: Ghiles Reguig (Correspondant), Marco Lorenzi.

The aim of this project is in the development of robust analysis tools for neuroimaging data, that can

find application in multi-centric studies such as the French CATI. We are currently focusing on:

• Automatically harmonize and standardize data from various french centers using CATI (Centre

d’Acquisition et de Traitement d’Images pour la maladie d’Alzheimer) data.

• Develop decentralized algorithms (using federated learning and the Fed-BioMed package) to
conduct data analysis/hypothesis testing using different types of data (MRI, omics), as shown in
Figure 28.

Figure 28: The federated framework that will be organized by StratifyAging in the long run. Several cloud-
based platforms will host the datasets of interest. Data-driven harmonization procedures or stratification
algorithms can be triggered on a selection of datasets using a federated strategy. The exact set of platforms
will be defined in agreement with the sponsors and the PIs of the studies.

8.5.6 Federated Data Harmonization in Biomedical Research Using Mixed Effects Models: a Focus on

Conditional Variational Autoencoders

This work has been funded by H2020/Marie Sklodowska-Curie Actions COFUND number 847579.

Keywords Federated learning; Harmonization; Biomedical data; Brain imaging; VAE.

Participants:

Santiago Silva (Correspondant), Marco Lorenzi.

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Inria Annual Report 2023

Bias correction through harmonization is a crucial step in multicentric neuroimaging studies, as
bias is introduced from sources like acquisition protocols and varied scanner types. In addition, with
the introduction of data privacy regulations, data preprocessing steps require to be adapted to respect
data governance. Traditional harmonization methods such as surrogate variable analysis and ComBat,
limited by their inability to capture nonlinearities, and also conflict with data privacy laws due to their
centralized data reliance. Federated learning (FL) can be used as a solution, enhancing data governance
and compliance with privacy regulations. To tackle this problem we focused on the following actions:

• We introduced a harmonization approach based on a FL paradigm and conditional variational
autoencoders (CVAEs) for decentralized data harmonization, addressing the aforementioned limit-
ations and managing nonlinear relationships. The CVAE proposed acts as a mixed effects model
whose random effects capture the multicentric biases (Figure 29).

• We presented preliminary findings on synthetic data and outline plans for extending our methodo-
logy to imaging applications, highlighting the potential and challenges in this innovative approach.

• The ensemble of contributions were defended in the doctoral dissertation [53].

Figure 29: Architecture of the Conditional Variational Autoencoder (CVAE) for data harmonization. The
input data x consists of a design matrix containing both phenotypes and covariates, with the goal of
preserving covariate effects. The conditioning variable y influences the generation process, while the
latent variables z capture underlying representations. The decoder, parametrized by θ, reconstructs
the input data based on the latent variables and conditioning variable. The encoder, parametrized by
φ, captures the approximate posterior or inference model q(z|x, y; φ). In the bottom part, an example
illustrates the latent space of the CVAE. Conditioning on the batch effects ensures that the latent space
remains consistent, removing residual batch information.

EncoderDecoderReparameterization trickDesign matrix of unharmonizedphenotypes and covariatesReconstructed design matrix afterconditioned generative processBatch indicator matrixThe latent space is forced tobe the same afterconditioning on the batcheffects meaning, no residualbatch information is kept inthe latent spacePhenotypesCovariatesProject EPIONE

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8.5.7 Data Structuration and Security in Large-scale Collaborative Healthcare Data Analysis

This work has been supported by the French government, through the 3IA Côte d’Azur Investments.

Keywords Federated learning; Biomedical application; Privacy preserving machine learning.

Participants: Riccardo Taiello (Correspondant), Marco Lorenzi, Melek Önen,

Olivier Humbert.

Our work aims to study the impact of privacy-preserving methods in Federated Learning for biomed-

ical applications:

• We extended Privacy Preserving Image Registration, with new cost function Figure 30;

• We proposed a new cryptographic protocol for Secure Aggregation in Federated Learning, tackling

the problem of drop-out clients;

• We implemented Secure Aggregation scheme for Federated Learning in Fed-BioMed [63].

Figure 30: Privacy Preserving Image Registration. The red frame is the transformed moving image using
the classic image registration. Green and Yellow frames are the transformed images using two different
privacy-preserving tools.

8.5.8 Federated Unlearning

This project received financial support by the INRIA Defi Fed-Malin.

Keywords Machine unlearning; Neural network.

Participants: Martin Van Waerebeke (Correspondant), Marco Lorenzi, Kevin Sca-
man (Inria DYOGENE), Giovanni Neglia (Inria NEO).

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Inria Annual Report 2023

This project studies the problem of machine unlearning, where we aim at removing the effect of a
given data instance from a trained model, to guarantee the “right to be forgotten” in AI systems. Our
contributions are the following:

• By using previous results on the contractivity of gradient descent under certain assumptions, we
lowered the strength of the assumptions required to prove the main theorem of the paper. We also
derived new bounds for an even larger assumptions set.

• To demonstrate the practical effectiveness of our algorithm, we conducted a series of new exper-
iments. They were used to prove the empirical validity of some assumptions made in the paper.
They also showcased that our results outperform competing methods when acting under time
constraint for unlearning, as can be observed in Figure 31.

• A core aspect of this research was undertaking a comprehensive review of the field. This involved
comparing the working assumptions and methodologies of different approaches within the domain.
It allowed us to showcase the small amount of methods involving both realistic assumptions and
theoretical guarantees in the federated unlearning setup.

Figure 31: Comparing retain set and forget set accuracies after unlearning for a limited amounf of time
on CIFAR100 for several methods in the field.

9 Bilateral contracts and grants with industry

9.1 Bilateral contracts with industry

9.1.1 Koelis

Participants: Nicholas Ayache, Hervé Delingette.

The company Koelis participates in the thesis work of Zhijie Fang and Manasi Kattel on the multimodal
registration of MR and ultrasound images. The objective is to improve the accuracy of the targeted
biopsies inside the prostate.

Project EPIONE

9.1.2 Accenture Labs

Participants: Marco Lorenzi, Yann Fraboni.

43

Accenture Labs, Sophia Antipolis, France, has funded the CIFRE PhD work of Yann Fraboni, which
aims at investigating the problem of bias and fairness in federated learning applications. Accenture also
supports the development of the platform Fed-BioMed.

9.1.3 MyDataModels

Participants: Marco Lorenzi.

MyDataModels, Sophia Antipolis, France, collaborates with Epione within the context of the postdoc-
toral project of Ghiles Reguig, aiming at developing a federated platform for the analysis of medical
records in a network of hospitals in France.

9.1.4 Microport CRM

Participants: Maxime Sermesant, Gaëtan Desrues.

Microport CRM is a cardiac impantable device company, and has funded the PhD work of Gaëtan
Desrues, which aims at using cardiac electromechanical modeling for cardiac resynchronization therapy
planning.

9.1.5 ExactCure

Participants:

Irene Balelli, Maxime Sermesant, Erwan Gaymard.

ExactCure, Nice, France, has funded the CIFRE PhD work of Erwan Gaymard, which aimed at the
development of novel methods for meta-modeling in pharmacometrics, to allow the co-integration of
highly heterogeneous sources. This PhD was interrupted.

9.1.6 Spin-off company inHEART

Participants: Maxime Sermesant.

inHEART is a spin-off of the Epione team and IHU Liryc funded in 2017. inHEART provides a service
to generate detailed anatomical and structural meshes from medical images, that can be used during
ablation interventions. inHEART received 2 awards, one from Aquitaine region and one i-LAB from the
BPI. It raised 3.2 million euros in 2020. It currently employs 27 people. It is FDA and CE certified.

10 Partnerships and cooperations

10.1 International collaborations

Laboratory of Physics of Fluids, University of Twente, NL Hervé Delingette is collaborating with Assist-
ant Professor Guillaume Lajoinie, on the topics of Deep Learning for ultrasound imaging in the
framework of the BoostUrCareer Cofund program and the thesis of Hari Sreedhar.

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Inria Annual Report 2023

King’s College London (KCL), London, UK Marco Lorenzi is a collaborator of the School of Biomedical
Engineering & Imaging Sciences at King’s College London. This collaboration focuses on the
development of novel collaborative learning methods to improve prostate cancer detection in
multicentric studies.

University College London (UCL), London, UK Marco Lorenzi is a collaborator of the COMputational
Biology in Imaging and geNEtics (COMBINE) group within the Centre for Medical Image Computing
(CMIC) of UCL. His collaboration is on the topic of spatio-temporal analysis of medical images and
imaging-genetics, with special focus on brain imaging analysis and biomarker development.

Laboratory of Neuroimaging of Aging (LANVIE), Faculty of Medicine, Geneva University Hospitals, HUG

Marco Lorenzi collaborates with the LANVIE laboratory led by Prof. Giovanni B. Frisoni. The collab-
oration consists in developing and translating novel approaches for disease progression modeling
in neurodegenerative disorders, such as Alzheimer’s disease.

Illinois Institute of Technology (IIT, IL, USA) Marco Lorenzi is currently a collaborator of IIT for the
investigation of the complex relationship between brain atrophy and genetics in Alzheimer’s disease,
in particular for demonstrating the effectiveness of multivariate statistical models in providing a
meaningful description of the relationship between genotype and brain phenotype.

Department of Computer Science, University of Copenhagen, DK Xavier Pennec is collaborating with
Pr. Stefan Sommer on stochastic and sub-Riemannian approaches to statistics, in the framework of
his ERC G-statistics. They jointly advised the PhD thesis of Morten Akhoj Pedersen.

10.1.1 Participation in other International Programs

Project MDPARK, funded by Michael J. Fox Fundation

Participants: Marco Lorenzi.

Title: Multimodal Dynamic Modeling and Prediction of Parkinsonian Symptom Progression

Partner Institution(s):

• Illinois Institute of Technology, USA

• Amsterdam University Medical Center, The Netherlands

• University of Southern California, USA

• Northwestern University School of Medicine, USA

Date/Duration: 2022-2026

Additionnal info/keywords: Connectomics, Disease models, Network analysis, Neuroimaging

10.2 International research visitors

10.2.1 Visits of international scientists

Hervé Lombaert

Status Associate Professor of Computer Engineering

Institution of origin: ETS Montreal

Country: Canada

Dates: Jan-Feb 2023; Nov-Dec 2023

Context of the visit: Collaboration with ETS Montreal, Canada

Mobility program/type of mobility: Research stay

Project EPIONE

45

10.3 European initiatives

10.3.1 Horizon Europe

EUCAIM

Participants: Marco Lorenzi.

Title: EUCAIM

Type: H2020

Program: Research and Inoovation

Duration: 2023-2027

Inria contact: Marco Lorenzi

Coordinator: University of Valencia, EIBR

Summary: EUropean Federation for CAncer IMages (EUCAIM) is the cornerstone of the European
Commission-initiated European Cancer Imaging Initiative, a flagship of Europe’s Beating Cancer
Plan (EBCP), which aims to foster innovation and deployment of digital technologies in cancer treat-
ment and care to achieve more precise and faster clinical decision making, diagnostics, treatment
and predictive medicine for cancer patients.

EUCAIM will establish and deploy a pan-European digital federated infrastructure of FAIR pan-
cancer anonymized images from Real-World Data, preserving the data sovereignty of providers.
Furthermore, it will provide an Experimentation Platform for the development and benchmarking
of AI tools toward Precision Medicine in cancer diagnosis and treatment. EUCAIM will address the
fragmentation of the existing cancer image repositories by building a distributed atlas of cancer
images, including both common and rare types of cancer, from existing initiatives, including related
Research Infrastructures’ networks and successful Horizon 2020 projects. EUCAIM will target
clinicians, researchers, and innovators, providing the means to build reproducible clinical decision-
making systems supporting diagnosis, treatment, and predictive medicine. This Infrastructure will
benefit citizens through improved healthcare procedures, and will stimulate the European market
through the innovation of new tools and services.

10.3.2 H2020 projects

ERC G-Statistics

Participants:

Xavier Pennec.

Title: Geometric Statistics

Type: ERC

Program: H2020

Duration: 2018-2024

Inria contact: Xavier Pennec

Coordinator: Inria

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Inria Annual Report 2023

Summary: G-Statistics aims at exploring the foundations of statistics on non-linear spaces with applic-
ations in the Life Siences. Invariance under gauge transformation groups provides the natural
structure explaining the laws of physics. In life sciences, new mathematical tools are needed to
estimate approximate invariance and establish general but approximate laws. Rephrasing Poincaré:
a geometry cannot be more true than another, it may just be more convenient, and statisticians
must find the most convenient one for their data. At the crossing of geometry and statistics, G-
Statistics aims at grounding the mathematical foundations of geometric statistics and to exemplify
their impact on selected applications in the life sciences. So far, mainly Riemannian manifolds
and negatively curved metric spaces have been studied. Other geometric structures like quotient
spaces, stratified spaces or affine connection spaces naturally arise in applications. G-Statistics
will explore ways to unify statistical estimation theories, explaining how the statistical estimations
diverges from the Euclidean case in the presence of curvature, singularities, stratification. Beyond
classical manifolds, particular emphasis will be put on flags of subspaces in manifolds as they ap-
pear to be natural mathematical object to encode hierarchically embedded approximation spaces.
In order to establish geometric statistics as an effective discipline, G-Statistics will propose new
mathematical structures and characterizations of their properties. It will also implement novel
generic algorithms and illustrate the impact of some of their efficient specializations on selected
applications in life sciences. Surveying the manifolds of anatomical shapes and forecasting their
evolution from databases of medical images is a key problem in computational anatomy requiring
dimension reduction in non-linear spaces and Lie groups. By inventing radically new principled
estimations methods, we aim at illustrating the power of the methodology and strengthening the
“unreasonable effectiveness of mathematics” for life sciences.

SimCardioTest

Participants: Maxime Sermesant, Irene Balelli, Jairo Rodriguez, Safaa Al Ali.

Title: Simulation of Cardiac Devices & Drugs for in-silico Testing and Certification

Link: SimCardioTest website; SimCardioTest project on cordis.europa.eu

Duration: From January 1, 2021 to December 31, 2024

Partners:

• Universidad Pompeu Fabra (UPF), Spain

• Institut national de recherche en informatique et en automatique (Inria), France

• Virtual Physiological Human Institute for Integrative Biomedical Research VZW (VPH Insti-

tute), Belgium

• Sorin CRM SAS (LIVANOVA), France

• EXACTCURE, France

• Simula Research Laboratory AS, Norway

• INSILICOTRIALS Technologies BV, Netherlands

• Université de Bordeaux (UBx), France

• Boston Scientific Scimed INC, United States

• Universitat Politècnica de València (UPV), Spain

• INSILICOTRIALS Technologies S.P.A. (InSilicoTrials), Italy

Inria contact: Maxime Sermesant

Coordinator: Inria

Project EPIONE

47

Summary: Despite massive investment in healthcare, huge R&D cost increase and regulatory pathway
complexity hamper tremendously commercialization of new devices & medicines, putting pa-
tient populations at risk of not receiving adequate therapy. At the same time, outside healthcare,
computer modeling and simulation (CM&S) is precisely recognized to increase speed & agility
while reducing costs of development. CM&S can create scientific evidence based on controlled
investigations including variability, uncertainty quantification, and satisfying demands for safety,
efficacy & improved access.

Cardiac modeling has dramatically gained maturity over the last decades, with personalization to
clinical data enabling validation. We selected a number of cardiac devices and medicines where
CM&S is mature enough and that represent the most common cardiac pathologies, to demonstrate
a standardized and rigorous approach for in-silico clinical trials.

SimCardioTest will bring a disruptive innovation by creating an integrated and secure platform
standardizing & bridging model simulations, in-silico trials, and certification support. This environ-
ment will go beyond the state-of-the-art in computational multi-physics & multi-scale personalized
cardiac models. Diseased conditions and gender/age differences will be considered to overcome
clinical trials limitations such as under-representation of groups (e.g. women, children, low socio-
economic status). Advanced big data, visual analytics & artificial intelligence tools will extract the
most relevant information.

It is critical that Europe demonstrates its capacity to leverage in-silico technology in order to
be competitive in healthcare innovation. SimCardioTest exploitation aims at delivering a major
economic impact on the European pharmaceutical and cardiac devices industry. It will accelerate
development, certification and commercialization, and will produce a strong societal impact
contributing to personalized healthcare.

inEurHeart

Participants: Maxime Sermesant.

Title: inEurHeart: AI, Digital Twin & Clinical Trial for a Disruption in Catheter Ablation

Link: inEurHeart website

Duration: 2022 - 2024

Partners:

• Institut national de recherche en Informatique et Automatique (Inria), France

• Rotterdam University, Netherlands

• Inserm, France

• CHU Bordeaux, France

• inHEART, France

• Université de Bordeaux (UBx), France

Inria contact: Maxime Sermesant

Coordinator: Inria

Summary: inEurHeart is an innovation project in Artificial Intelligence, Digital Twin & a Clinical Trial for
a Disruption in Catheter Ablation for Ventricular Tachycardia, making ablation therapy accessible
to most patients. This project is a collaborative project between 5 organizations in France and Neth-
erlands funded by EIT Health – the European Institute of Innovation and Technology, co-funded
by the European Union. This project will exemplify how the academic-industrial relationships
can be fostered and can lead to drastic changes in clinical practice. EIT Health provides a unique
opportunity to transfer Artificial Intelligence tools to enable the scale-up phase, and to validate the
technology through a randomized clinical trial.

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Inria Annual Report 2023

10.3.3 Other european programs/initiatives

TRAIN

Participants: Marco Lorenzi.

Title: Trustworthy and Robust Artificial INtelligence

Type: German-French joint call for proposals on Artificial Intelligence

Duration: 2023-2027

Inria contact: Marco Lorenzi

Coordinator: Inria

Summary: The goal of TRAIN is to explore two main barriers to the widespread deployment of AI: the
lack of trustworthiness and the lack of robustness. TRAIN, comprising of Inria and EURECOM
from France and RUB and Fraunhofer IPT from Germany, will design, develop and evaluate new
AI solutions that address these two challenges. The development of such AI systems requires
careful attention at every aspect of the model development pipeline, starting from construction
and training of models all the way to evaluation and deployment. The proposed research agenda
focuses on the more challenging federated learning (FL) setting as a key paradigm to enable
trustworthiness in the training phase. We also investigate trustworthiness and robustness of the
evaluation and deployment phase in both centralized and collaborative machine learning. The
project identifies four main objectives: (i) robust learning with data heterogeneity and bias; (ii)
quantifying robustness with automated frameworks; (iii) privacy-preserving federated learning; (iv)
adversarial robustness. TRAIN solutions will be integrated into real-world frameworks in two main
application domains: healthcare and smart industry.

10.4 National initiatives

10.4.1 PEPR Digital Health ChroniCardio

Participants:

Jairo Rodriguez, Mihaela Pop, Maxime Sermesant.

Duration: 2023 - 2027

Partners:

• Inria

• Hospices Civils de Lyon

• Assistance Publique - Hôpitaux de Marseille

• CREATIS

Inria contact: Maxime Sermesant

Coordinator: Inria

Summary: ChroniCardio is a new 4-year multi-institution project funded by the French Research Priority
Programme on Digital Health to accelerate the integration of multi-scale data (clinical, imaging,
genetic, ECG, etc.) and the development of advanced modeling tools to predict the long-term
evolution of non-ischemic dilated and hypertrophic cardiomyopathies. This includes the risk of
arrhythmia, heart failure and sudden cardiac death. Our consortium brings together research
scientists and engineers from Inria (Sophia Antipolis, Lyon, Rennes, Bordeaux) and INSA / Lyon
/University / CNRS (Lyon), and clinicians from Lyon and Marseille University Hospitals. The project
is coordinated by Maxime Sermesant, from Inria Epione team.

Project EPIONE

49

10.4.2 PEPR Digital Health Rewind

Participants: Marco Lorenzi.

Duration: 2023 - 2027

Partners:

• Inria

• CNRS

• INSERM

• Université Grenoble Alpes

Inria contact: Stephanie Allassonniére

Coordinator: Inria

Summary: The project Rewind will focus on the development of new mathematical and statistical
approaches for the analysis of multimodal multiscale longitudinal data. These models will be
designed, implemented as prototypes and then transferred to an easy-used-well-documented
platform where researchers from diverse communities, in particular physicians, will be able to
analyze their own data set.

10.4.3 PEPR Digital Health Secure, safe and fair machine learning for healthcare

Participants: Marco Lorenzi.

Duration: 2023 - 2027

Partners:

• Inria

• LAMSADE (CNRS, Dauphine-PSL)

• CEA

Inria contact: Aurelien Bellet

Coordinator: Inria, Dauphine-PSL

Summary: The goal of this project is to overcome the challenges that prevent the effective use of per-
sonalized health data. To achieve this, we will develop new machine learning algorithms that are
designed to handle the unique characteristics of multi-scale and heterogeneous individual health
data, while providing formal privacy guarantees robustness against adversarial attacks and changes
in data dynamics, and fairness for under-represented populations. By addressing these barriers, we
hope to unlock the full potential of personalized health data for a wide range of applications.

10.4.4 PEPR Digital Health Stratigy Aging

Participants: Marco Lorenzi.

Duration: 2023 - 2027

Inria Annual Report 2023

50

Partners:

• CEA

• Inria

• CNRS

• INSERM

Inria contact: Marco Lorenzi

Coordinator: CEA

Summary: The goal of StratifyAging is to focus on using high-quality, curated data from clinical research
to advance the field of patient stratification through the use of hypothesis-driven approaches or
AI algorithms. By harmonizing and aggregating data from various studies, it will be possible to
reach a large enough sample size to effectively stratify patients. This process will also lead to the
development of standard protocols that can be applied in routine care. As a result, a virtuous cycle
may be created, in which standardized data from routine care is collected and analyzed through
the Health Data Hub and used to perform population monitoring and develop normative charts
and decision support tools.

10.4.5 Other national initiatives

Consulting for Industry

• Nicholas Ayache has joined the Scientific Advisory Board of Caranx Medical in Oct 2021.

• Maxime Sermesant is a scientific advisor for the company inHEART (Bordeaux).

Institute 3IA Côte d’Azur

The 3IA Côte d’Azur is one of the four "Interdisciplinary Institutes of Artificial Intelligence" that
were created in France in 2019. Its ambition is to create an innovative ecosystem that is influential
at the local, national and international levels, and a focal point of excellence for research, education
and the world of AI.

Epione is heavily involved in this institute since 5 permanent researchers (N. Ayache, H. Delingette,
M. Lorenzi, M. Sermesant and X.Pennec) are chair holders in this institute, and N. Ayache serves as
scientific director. The 5 Epione chairs were renewed in 2023 by an international jury. H. Delingette
and N. Ayache are members of its scientific committee.

Funded projects

• Hervé Delingette is among the main investigators of the DAICAP project (2020-2025, 300k€) selected
by the Health Data Hub, the Grand Défi « Amélioration des diagnostics médicaux par l’Intelligence
Artificielle », and Bpifrance in July 2020. This project aims to develop an algorithm able to produce
a standardized MRI scan report to improve the early detection of prostate cancer.

• Hervé Delingette, Xavier Pennec and Maxime Sermesant are involved in the MEDITWIN project,
funded by the national program France-2030 and which aims to create digital twins for improving
the medical practice. This project launched in November 2023, gathers several research teams of
Inria, the company Dassault Systemes and IHU-France.

• Hervé Delingette is a member of the REBONE RHU project lead by Nice University Hospital (Pr

M-O. Gauci) focusing on computer-aided surgical tools for orthopaedics and traumatology.

Project EPIONE

51

• Marco Lorenzi is principal investigator of the ANR JCJC project Fed-BioMed (2020-2023). He is
co-PI of the project FEDERATED-PET (2022-2026), with Prof. Olivier Humbert (CAL, Nice). The
project is funded by the Institut National du Cancer (INCa), and aims at developing the first French
federated learning infrastructure in a network of hospitals from the Unicancer consortium. Marco
Lorenzi is also principal investigator of the project TRAIN, funded by the ANR, and co-PI of the
project StratifyAging of the PEPR Santé Numerique. He is also partner of the Horizon Europe Project
EUCAIM.

Collaboration with national hospitals

• The Epione project team collaborates with the following 3 French IHU (University Hospital In-
stitute): the IHU-Strasbourg (Pr J. Marescaux and L. Soler) on image-guided surgery, the IHU-
Bordeaux (Pr M. Haïssaguere and Pr P. Jaïs) on cardiac imaging and modeling and the IHU-Pitié
Salpétrière (Dr. O. Colliot and Pr. B. Stankoff) on neuroimaging.

• The IHU RespirERA was selected in May 2023 among the 12 new institutes in France. This IHU is
based in Nice, and focuses on respiratory diseases. Inria is one of its 4 founding institutions together
with the University Hospital of Nice, the Université Côte d’Azur and INSERM. Hervé Delingette and
Nicholas Ayache are the members of the executive team and are leading a workpackage focusing
on AI algorithms for data analysis.

• The Epione project team is involved in the following research projects with the Assistance Publique
des Hôpitaux de Paris (AP-HP) : NHANCE project on abdominal ultrasound image analysis with
Dr Anne-Laure Rousseau (Hospital St-Louis), PAIMRI project on prostate cancer detection with
Pr Raphaele Renard-Penna (Hospital La Pitié Salpêtrière), CLARITI project on PET-CT anomaly
detection with Pr Florent Besson (Hospital Kremlin Bicêtre).

• We also have long term collaborations with the CHU Nice, the Centre Antoine Lacassagne of Nice,

and the Hospital Lenval of Nice.

10.5 Regional initiatives

Hervé Delingette is the principal investigator of the I-Demo project PLICIA funded by the region Sud
and BPI France involving the companies Quantificare (Project lead), and Hosteur and the i3S laboratory
(Lionel Filatre). This project was accepted in October 2023.

11 Dissemination

Participants: Nicholas Ayache, Irene Balelli, Hervé Delingette, Marco Lorenzi,

Xavier Pennec, Maxime Sermesant.

11.1 Promoting scientific activities

11.1.1 Scientific events: organisation

• H. Delingette is a member of the organization commitee of the conference MIDL 2024 (Medical

Imaging with Deep Learning conference) that will take place in Paris in July 2024.

• M. Lorenzi organized a symposium “Federated Learning in Healthcare” as part of dissemination

plan of the Atelier FedSed MADICS (May 24th).

• X. Pennec organized the annual mathematical workshop on the geometry of shapes Math in the
Mine, July 16-23, 2023, at the Minière de Vallauria, Alpes Maritimes, FR. This one-week workshop
regroups about 25 people.

• M. Sermesant organized a workshop “AI & Digital Twin” for the European project inEurHeart, at

Inria, Sophia Antipolis, October 9-10, 2023.

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Inria Annual Report 2023

General chair, scientific chair

• Marco Lorenzi was Area Chair of the conferences Neural Information Processing Systems - NeurIPS

2024, and Computer Vision and Pattern Recognition - CVPR 2023.

11.1.2 Scientific events: selection

Member of the conference program committees

• M. Lorenzi was Organizing committee member of the 2023 edition of the IEEE International

Symposium on Biomedical Imaging (ISBI), Cartagena (Colombia).

• X. Pennec was member of the scientific committe and session chair at GSI 2023, Saint-Malo Aug.

30-Sep. 2 2023.

11.1.3 Journal

Member of the editorial boards

• N. Ayache is the co-founder and the Co-Editor in Chief with J. Duncan (Professor at Yale) of Medical
Image Analysis journal. This scientific journal was created in 1996 and is published by Elsevier.

• N. Ayache is a member of the editorial board of the following journal: Journal of Computer Assisted

Surgery (Wiley).

• H. Delingette is a member of the editorial board of the journal Medical Image Analysis (Elsevier).

• M. Lorenzi is member of the editorial board of the journal Medical Image Analysis (Elsevier). He is

also member of the Board of Statisticians of the Journal of Alzheimer’s Disease (IOS Press).

• X. Pennec is a member of the editorial board of the journal Medical Image Analysis (MedIA, Elsevier),
of the International Journal of Computer Vision (IJCV, Springer), and of the Journal of Mathematical
Imaging and Vision (JMIV, Springer).

• I. Strobant is editorial coordinator for Medical Image Analysis, Elsevier (since october 2001).

Reviewer - reviewing activities

• I. Balelli was a reviewer for the following journals: Vaccine (Elsevier), Medical Image Analysis

(Elsevier), Neuroimage (Elsevier), SMAI J. of Computational Mathematics.

• H. Delingette was a reviewer for the following journals: Medical Image Analysis (Elsevier), IEEE

Access.

• M. Lorenzi was a reviewer for the following journals: Journal of Alzheimer’s Disease, Medical Image
Analysis, IEEE Transactions on Medical Imaging, NeuroImage, International Journal of Computer
Vision.

• X. Pennec was a reviewer for the following journals: MedIA, SIAM Journal on Mathematics of Data

Science (SIMODS).

• M. Sermesant was a reviewer for the following journals: Journal of Machine Learning Research,
Journal of the American College of Cardiology, IEEE Transactions on Medical Imaging, IEEE Transac-
tions on Biomedical Engineering, Medical Image Analysis and Computers in Biology and Medecine.

Project EPIONE

11.1.4 Invited talks

53

• I. Balelli gave an invited talk at Innovaheart 2023 workshop, Bordeaux, March 22, 2023.

• H. Delingette was an invited speaker at the AI satellite symposium of the IEEE IUS conference in
Montreal, CA on Sept. 8th, at the Journées Françaises de radiologie (JFR’2023) on Oct. 14th, at
the Liliane Bettancourt Inserm doctoral school in Paris, on March 18th, at the French-Canadian
biobanking meeting in Nice on Sept. 22nd, at the FHU Plan-GO kick-off meeting in Nice on Dec.
13th.

• X. Pennec was invited speaker at the Research Center for Statistics (RCS) Seminar, Geneva School
of Economics and Management, U. of Geneva, Dec. 1, 2023 ; the Statistics of Shapes and Geometry
of Shape Spaces, MPI, Leipzig (DE), April 12 - 14, 2023 ; the Seminar of Probability and Statistics
team, Dieudonné (JAD) Lab, Nice University, March 21, 2023.

11.1.5 Scientific expertise

• H. Delingette is a member of the scientific committee of the institute 3IA Côte d’Azur and was
reviewer of the funding agency ANR (Agence Nationale de la Recherche, France), of the funding
agency ANRT (Association Nationale Recherche Technologie). He was also a member of the working
group organized the HAS (Haute Autorité de la Santé) for the creation of a guide to help health
professional choose digital medical devices and he was a project reviewer for the HAS.

• M. Lorenzi was in the selection committee for the France-Japan call for proposals on Edge Artificial
Intelligence, and external Advisory Board of the HealthData@EU Pilot project. He was also reviewer
for the Inria Associate Teams Program.

11.1.6 Research administration

• N. Ayache is a member of the scientific council of the Health Data Hub. He is also chair of the

Scientific Committee of the 3IA Co-d’Azur.

• I. Balelli is a member of the scientific advisory board of the GIS (scientific interest group) FC3R since
Jul. 2023, and of the Scientific committee of the Academy 2 (Complex Systems) since Nov. 2023. I.
Balelli is in charge of the pedagogical orgization of the AI for Health track of the Data Science & AI
Master, Univ. Côte d’Azur, France.

• H. Delingette is one of the 3 scientific directors of the IdEx program UCA JEDI under the direction
of the IdEX vice-president of the Université Côte d’Azur. He is an administrator and a member of
the scientific committee of the Groupement de Coopération Sanitaires (GCS) CARES involving the
Université Côte d’Azur and the 3 local hospitals (CHU Nice, Centre Antoine Lacassagne, Fondation
Lenval). He is the president of the CUIV (Commission UniCA Innovation et Valorisation) which
ensures the coordination among the technology transfer and innovation departments of IdEx
members, of the evaluation of innovation and valorization projects. He is also a member of the
research and innovation committee organized by the employer union UPE06.

• H. Delingette is a representive of Inria at the Federation Hospitalo-Universitaire Oncoage led by the
CHU Nice. He is also the contact person at the Inria center of Université Côte d’Azur for research
data management. H. Delingette is the Inria representative at the executive committee of the
DATAZUR structure, helping Université Côte d’Azur researchers handle their research data.

• M. Lorenzi is member of the scientific committee of the AI4Health Winter School, and of the

Colloque Français d’Intelligence Artificielle en Imagerie Biomédicale (IABM 2023).

• M. Milanesio is a member of DATAZUR, a project to integrate and actively participate to the
Research Data Gouv ecosystem, which aims at depositing, curating and publishing final research
datasets in open access or shared on a restricted basis if there are legitimate legal exceptions
like professional and industrial secrecy or in the case of personal data. The final project will be
submitted on February 2024.

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Inria Annual Report 2023

• X. Pennec is co-director of the Ecole doctorale STIC of Université Côte d’Azur. He is a member of
the committee of EDSTIC, of the Doctoral follow-up Committee (CSD) at Inria Sophia Antipolis,
and participated to the PhD fellowship granting committees of EDSTIC and CORDI at Inria.

• X. Pennec was a member of the "Comité de la Recherche Biomédicale en Santé Publique (CRBSP)"
of the Nice University Hospital (CHU). At the University Côte d’Azur / UCA JEDI, he is a member
of the executive committee of the Academy 4 (Living systems Complexity and diversity), of the
Scientific committee of the Academy 2 (Complex Systems), and of the Advanced Research Program
Committee. He was a member of the Evaluation Committee of Inria until August 2023.

11.2 Teaching - Supervision - Juries

11.2.1 Teaching

• Master: F. Cremonesi and Y. Bouillard, Federated Learning, 18h ETD, École d’ingénieur ISIS, Castres,

France

• Master: H. Delingette and X. Pennec, Introduction to Medical Image Analysis, 21h course (28.5

ETD), Master 2 MVA, ENS Saclay, France.

• Master: H. Delingette, 3h, Challenges in AI in Digital Pathology, Msc Biobank, Univ. Côte d’Azur,

France.

• Master: M. Lorenzi and I. Balelli, Bayesian Learning, 30h course, Master Data Science, Univ. Côte

d’Azur, France.

• Master: M. Lorenzi, Model Selection and Resampling Methods, 30h course, Master Data Science,

Univ. Côte d’Azur, France.

• Master: L. Innocenti, F. Cremonesi and M. Lorenzi, 3h ETD, Federated Learning with Fed-BioMed,

University College Dublin, Ireland

• Licence: I. Balelli, Advanced statistical modeling, 22.5h ETD, Univ. Côte d’Azur, France.

• University Diploma in Healthcare : H. Delingette, 1h, Challenges in AI, Univ. Côte d’Azur, France.

• L. Innocenti and F. Cremonesi presented a two-day workshop on Federated Learning and the

Fed-BioMed software at the AI4Health Summer School in Paris.

11.2.2 Supervision: defended PhDs

• Gaëtan Desrues, 3D electromechanical cardiac modelling for heart failure patients stratification
and prediction of cardiac resynchronization therapy response, Université Côte d’Azur. CIFRE
fellowship with Microport CRM. Directed by Maxime Sermesant. Defended on March 28th, 2023
[46].

• Yann Fraboni, Bias in Federated Learning, Université Côte d’Azur. CIFRE fellowship with Accenture

Labs. Directed by Marco Lorenzi. Defended on May 11th, 2023 [47].

• Dimitri Hamzaoui, AI-Based Diagnosis of Prostate Cancer from Multiparametric MRI, Université
Côte d’Azur. Co-directed by Nicholas Ayache and Hervé Delingette. Defended on June 26th, 2023
[48].

• Etrit Haxholli, Exploring latent dynamical models for failure prediction in time-series of high-
dimensional and heterogeneous data, Université Côte d’Azur. Directed by Marco Lorenzi. Defended
on October 25th, 2023 [49].

• Victoriya Kashtanova, Learning Cardiac 3D Electromechanical Dynamics with PDE-based Physiolo-
gical Constraints for Data-Driven Personalized Predictions in Cardiology, Université Côte d’Azur.
Co-directed by Maxime Sermesant and Patrick Gallinari (Sorbonne University, LIP6, Paris). Defen-
ded on June 14th, 2023 [50].

Project EPIONE

55

• Elodie Maignan, Barycentric embeddings for geometric manifold learning with application to
shapes and graphs, Université Côte d’Azur. Co-directed by Xavier Pennec and Alain Trouvé (ENS
Paris-Saclay). Defended on December 6th, 2023, [51].

• Morten Akhoj Pedersen, Riemannian and sub-Riemannian methods for dimension reduction,
Université Côte d’Azur and University of Copenhagen. Co-directed by Xavier Pennec and Stefan
Sommer (Univ Copenhagen, DK). Defended on November 23rd, 2023 [52].

• Santiago Smith Silva Rincon, Federated learning of biomedical data in large-scale networks of
multicentric imaging-genetics information, Université Côte d’Azur. Co-directed by Marco Lorenzi
and Barbara Bardoni (INSERM). Defended on July 18th, 2023 [53].

• Paul Tourniaire, AI-based selection of imaging and biological markers predictive of therapy response
in lung cancer, Université Côte d’Azur. Co-directed by Nicholas Ayache and Hervé Delingette.
Defended on June 12th, 2023 [54].

• Yingyu Yang, Artificial Intelligence for Automatic Cardiac Function Analysis: Multimodal and
Biophysical Approach with Application to a Portable Imaging Device, Université Côte d’Azur. Co-
directed by Maxime Sermesant and Pamela Moceri (CHU-Nice). Defended on December 19th, 2023
[55].

11.2.3 Supervision: ongoing PhDs

• Olivier Bisson, Géométrie, Stratification et application des matrices de corrélation structurées.

Directed by X. Pennec. 3IA PhD started in October 2023.

• Federica Facente, Learning Statistical and Biomedical Models for multimodal image analysis –
application to Image Guided Surgical Robotics, 3IA PhD started in September 2023. Co-directed by
Nicholas Ayache, Pierre Berthet-Rayne (CTO and co-founder of Caranx-Medical, 3IA affiliate chair
holder) and Hervé Delingette.

• Zhijie Fang, Robust Multimodal Non rigid image registration based on machine learning, Université
Côte d’Azur. Started in 2020. Co-directed by Hervé Delingette and Nicholas Ayache. The PhD
terminated in Sept. 2023.

• Samy Ferrat, Automatic generation of three-dimensional models of extremity fractures proximal
humerus for preoperative planning and intraoperative assistance in mixed reality, Université Côte
d’Azur. Inria-Inserm Fellowship. Started in Dec. 2023. Co-directed by Hervé Delingette and
Marc-Olivier Gauci.

• Erwan Gaymard, Innovative mathematical methodologies in pharmacometric meta-modeling from
highly heterogeneous sources, Université Côte d’Azur. CIFRE fellowship with ExactCure. Started in
2022. Co-directed by Irene Balelli and Maxime Sermesant. This PhD was interrupted.

• Sebastien Goffart, Development of predictive models in patients with peripheral artery disease,
Université Côte d’Azur. ANR grant handled by CHU Nice. Started in 2023. Co-directed by Hervé
Delingette, and Juliette Raffort-Lareyre.

• Lisa Guzzi, Automatic segmentation of the vascular system to enhance AI-based decision support
system for peripheral artery disease, Université Côte d’Azur, 3IA felloship. Started in 2022. Directed
by Hervé Delingette, Juliette Raffort-Lareyre

• Josquin Harrison, Medical Imaging and learning for the prediction of strokes in the case of atrial

fibrillation, Université Côte d’Azur. Started in 2020. Directed by Maxime Sermesant.

• Lucia innocenti, Multi-centric AI-based framework for prostate cancer patients management on
active surveillance through robust federated learning. 3IA PhD started in June 2022. Co-directed by
Marco Lorenzi and Prof. Sebastien Ourselin (KCL, London).

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Inria Annual Report 2023

• Manasi Kattel, Deep learning methods for the analysis and registration of US images, Université
Côte d’Azur. 3IA Côte d’Azur fellowship. Started in December 2023. Co-directed by Nicholas Ayache
and Hervé Delingette.

• Mengda Li, Effective AI based Detection of Prostate Cancer from Multiparametric MRI, 3IA Côte
d’Azur fellowship. Started in September 2023. Co-directed by Nicholas Ayache and Hervé Delin-
gette.

• Huiyu Li, Anonymization and protection of medical data based on deep neural networks. Université

Côte d’Azur. Started in 2020. Co-directed by Hervé Delingette and Nicholas Ayache.

• Maelis Morier, Deep Learning Meets Numerical Modelling, AI and Biophysics for Computational
Cardiology, started in 2023. Co-supervised by Maxime Sermesant and Patrick Gallinari (Sorbonne
Universités).

• Rafael Luis Soares Da Costa E Silva, Artificial Intelligence for Cardiac Monitoring: Portable Mul-
timodal Cardiac Function Analysis, started in 2023. Co-directed by Maxime Sermesant and Pamela
Moceri (CHU Nice).

• Hari Sreedhar, Learning-based detection and classification of thyroid nodules from ultrasound
images, Université Côte d’Azur. Started in 2020. Co-directed by Hervé Delingette and Charles
Raffaelli (CHU-Nice).

• Tom Szwagier, Rethinking statistical methods with Flags spaces, Université Côte d’Azur. Started in

2022. Directed by Xavier Pennec.

• Riccardo Taiello, Data Structuration and Security in Large-Scale Collaborative Healthcare Data
Analysis, Université Côte d’Azur. Started in 2021. Co-supervised by Melek Önen (EURECOM) and
Olivier Humbert (Centre Antoine Lacassagne).

11.2.4 Juries

• N. Ayache was a member of PhD juries as co-supervisor of Dimitri Hamzaoui on June 26th and Paul

Tourniaire on June 12th.

• H. Delingette was a reviewer of the Habilitation jury of Hamid Ladjal at the Université Claude
Bernard Lyon 1 who defended on Feb. 9th 2023, and he was a member of the Habilitation jury of Y.
Chenoune at the Université Paris-Est Créteil on Nov. 28th 2023. He was a member of PhD juries as
supervisor of Dimitri Hamzaoui on June 26th and Paul Tourniaire on June 12th.

• H. Delingette was the Chair of the jury selecting a Junior professor at Université Côte d’Azur on the

topics of Frugal AI between April and June 2023.

• M. Lorenzi was a member and reviewer of the PhD jury of Lisa Bonheme (Univ. Kent, UK. Dec
2023), Hugo Schmutz and Giulia Marchiello (UniCA, Dec. 2023), Benoit Freiche (INSA Lyon, Dec.
2023), Benoit Sauty and Marina Costantini (Sorbonne Université, Sep. and May 2023).

• X. Pennec was a member of the PhD jury of Guillaume Olikier, Univ. Louvain, BE. May 2023.

11.3 Popularization

11.3.1 Articles and contents

The projects FEDERATED-PET and Fed-BioMed have been object of large dissemination in general
audience and clinical newspapers (e.g. Nice Matin).

11.3.2 Interventions

B. Ly, R. Silva, E. Njomgue, A. Zanella, M. Sermesant had a booth "Digital Heart" at the Science Festival,
6-7-8 Octobre, Villeneuve Loubet

Project EPIONE

57

12 Scientific production

12.1 Major publications

[1] N. Cedilnik, M. Pop, J. Duchateau, F. Sacher, P. Jaïs, H. Cochet and M. Sermesant. ‘Efficient Patient-
Specific Simulations of Ventricular Tachycardia Based on Computed Tomography-Defined Wall
Thickness Heterogeneity’. In: JACC: Clinical Electrophysiology (Sept. 2023). DOI: 10.1016/j.jace
p.2023.08.008. URL: https://inria.hal.science/hal-04255556.

[2] H. Dadoun, H. Delingette, A.-L. Rousseau, E. de Kerviler and N. Ayache. ‘Deep Clustering for Ab-
dominal Organ Classification in US imaging’. In: Journal of Medical Imaging 10.3 (2023), p. 034502.
DOI: 10.1117/1.JMI.10.3.034502. URL: https://inria.hal.science/hal-03773082.

[3] Y. Fraboni, R. Vidal, L. Kameni and M. Lorenzi. ‘A General Theory for Federated Optimization with
Asynchronous and Heterogeneous Clients Updates’. In: Journal of Machine Learning Research 24
(Mar. 2023), pp. 1–43. URL: https://hal.science/hal-03720629.

[4] N. Guigui, N. Miolane and X. Pennec. ‘Introduction to Riemannian Geometry and Geometric
Statistics: from basic theory to implementation with Geomstats’. In: Foundations and Trends in
Machine Learning 16.3 (Feb. 2023), pp. 329–493. DOI: 10.1561/2200000098. URL: https://inri
a.hal.science/hal-03766900.

[5] D. Hamzaoui, S. Montagne, R. Renard-Penna, N. Ayache and H. Delingette. ‘Morphologically-Aware
Consensus Computation via Heuristics-based IterATive Optimization (MACCHIatO)’. In: Journal of
Machine Learning for Biomedical Imaging 2.UNSURE 2022 Special Issue (14th Sept. 2023), pp. 361–
389. DOI: 10.59275/j.melba.2023-219c. URL: https://hal.science/hal-04208018.

[6] V. Kashtanova, M. Pop, I. Ayed, P. Gallinari and M. Sermesant. ‘Simultaneous data assimilation and
cardiac electrophysiology model correction using differentiable physics and deep learning’. In:
Interface Focus 13.6 (15th Dec. 2023). DOI: 10.1098/rsfs.2023.0043. URL: https://hal.scien
ce/hal-04359753.

[7] P. Tourniaire, M. Ilie, P. Hofman, N. Ayache and H. Delingette. ‘MS-CLAM: Mixed Supervision for
the classification and localization of tumors in Whole Slide Images’. In: Medical Image Analysis 85
(2023), p. 102763. DOI: 10.1016/j.media.2023.102763. URL: https://inria.hal.science/h
al-03972289.

12.2 Publications of the year

International journals

[8] A. Berenbaum, H. Delingette, A. Maire, C. Poret, C. Hassen-Khodja, S. Bréant, C. Daniel, P. Martel, L.
Grimaldi, M. Frank, E. Durand and F. Besson. ‘Performance of AI-Based Automated Classifications
of Whole-Body FDG PET in Clinical Practice: The CLARITI Project’. In: Applied Sciences 13.9 (May
2023), p. 5281. DOI: 10.3390/app13095281. URL: https://inria.hal.science/hal-0408104
6.

[9] N. Brillat-Savarin, C. Wu, L. Aupin, C. Thoumin, D. Hamzaoui and R. Renard-Penna. ‘3.0 T prostate
MRI: Visual assessment of 2D and 3D T2-weighted imaging sequences using PI-QUAL score’. In:
European Journal of Radiology 166 (Sept. 2023), p. 110974. DOI: 10.1016/j.ejrad.2023.110974.
URL: https://hal.science/hal-04162794.

[10] A. Calissano, T. Papadopoulo, X. Pennec and S. Deslauriers-Gauthier. ‘Graph Alignment Exploiting
the Spatial Organisation Improves the Similarity of Brain Networks’. In: Human Brain Mapping
45.1 (28th Dec. 2023), e26554. DOI: 10.1002/hbm.26554. URL: https://inria.hal.science/h
al-03910761.

[11] N. Cedilnik, M. Pop, J. Duchateau, F. Sacher, P. Jaïs, H. Cochet and M. Sermesant. ‘Efficient Patient-
Specific Simulations of Ventricular Tachycardia Based on Computed Tomography-Defined Wall
Thickness Heterogeneity’. In: JACC: Clinical Electrophysiology (Sept. 2023). DOI: 10.1016/j.jace
p.2023.08.008. URL: https://inria.hal.science/hal-04255556.

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Inria Annual Report 2023

[12] N. Chrisochoides, Y. Liu, F. Drakopoulos, A. Kot, P. Foteinos, C. Tsolakis, E. Billias, O. Clatz, N. Ayache,
A. Fedorov, A. Golby, P. Black and R. Kikinis. ‘Comparison of physics-based deformable registration
methods for image-guided neurosurgery’. In: Frontiers in Digital Health 5 (8th Dec. 2023). DOI:
10.3389/fdgth.2023.1283726. URL: https://inria.hal.science/hal-04334718.

[13] Q. Clairon, C. Pasin, I. Balelli, R. Thiébaut and M. Prague. ‘Parameter estimation in nonlinear
mixed effect models based on ordinary differential equations: An optimal control approach’. In:
Computational Statistics (18th Sept. 2023). URL: https://hal.science/hal-03335826.

[14] H. Dadoun, H. Delingette, A.-L. Rousseau, E. de Kerviler and N. Ayache. ‘Deep Clustering for Ab-
dominal Organ Classification in US imaging’. In: Journal of Medical Imaging 10.3 (2023), p. 034502.
DOI: 10.1117/1.JMI.10.3.034502. URL: https://inria.hal.science/hal-03773082.

[15] Y. Fraboni, R. Vidal, L. Kameni and M. Lorenzi. ‘A General Theory for Federated Optimization with
Asynchronous and Heterogeneous Clients Updates’. In: Journal of Machine Learning Research 24
(Mar. 2023), pp. 1–43. URL: https://hal.science/hal-03720629.

[16] N. Guigui, N. Miolane and X. Pennec. ‘Introduction to Riemannian Geometry and Geometric
Statistics: from basic theory to implementation with Geomstats’. In: Foundations and Trends in
Machine Learning 16.3 (Feb. 2023), pp. 329–493. DOI: 10.1561/2200000098. URL: https://inri
a.hal.science/hal-03766900.

[17] D. Hamzaoui, S. Montagne, R. Renard-Penna, N. Ayache and H. Delingette. ‘Morphologically-Aware
Consensus Computation via Heuristics-based IterATive Optimization (MACCHIatO)’. In: Journal of
Machine Learning for Biomedical Imaging 2.UNSURE 2022 Special Issue (14th Sept. 2023), pp. 361–
389. DOI: 10.59275/j.melba.2023-219c. URL: https://hal.science/hal-04208018.

[18] P. Harms, P. W. Michor, X. Pennec and S. Sommer. ‘Geometry of Sample Spaces’. In: Differential
Geometry and its Applications 90 (Oct. 2023), p. 102029. DOI: 10.1016/j.difgeo.2023.102029.
URL: https://inria.hal.science/hal-02972385.

[19] R. Hussain, A. Frater, R. Calixto, C. Karoui, J. Margeta, Z. Wang, M. Hoen, H. Delingette, F. Patou,
C. Raffaelli, C. Vandersteen and N. Guevara. ‘Anatomical Variations of the Human Cochlea Using
an Image Analysis Tool’. In: Journal of Clinical Medicine 12.2 (8th Jan. 2023), p. 509. DOI: 10.3390
/jcm12020509. URL: https://inria.hal.science/hal-04388909.

[20] V. Kashtanova, M. Pop, I. Ayed, P. Gallinari and M. Sermesant. ‘Simultaneous data assimilation and
cardiac electrophysiology model correction using differentiable physics and deep learning’. In:
Interface Focus 13.6 (15th Dec. 2023). DOI: 10.1098/rsfs.2023.0043. URL: https://hal.scien
ce/hal-04359753.

[21] F. Lareyre, K. K. Yeung, L. Guzzi, G. Di Lorenzo, A. Chaudhuri, C.-A. Behrendt, K. Spanos and
J. Raffort. ‘Artificial intelligence in vascular surgical decision making’. In: Seminars in Vascular
Surgery 36.3 (Sept. 2023), pp. 448–453. DOI: 10 . 1053 / j . semvascsurg . 2023 . 05 . 004. URL:
https://inria.hal.science/hal-04361618.

[22] S. Molière, D. Hamzaoui, A. Luzurier, B. Granger, S. Montagne, A. Allera, M. Ezziane, R. Quint,
M. Kalai, N. Ayache, H. Delingette and R. Renard-Penna. ‘Reference standard for the evaluation
of automatic segmentation algorithms: Quantification of inter observer variability of manual
delineation of prostate contour on MRI’. In: Diagnostic and Interventional Imaging (Aug. 2023).
DOI: 10.1016/j.diii.2023.08.001. URL: https://hal.science/hal-04185065.

[23] T. Soulier, O. Colliot, N. Ayache and B. Rohaut. ‘How will tomorrow’s algorithms fuse multimodal
data? The example of the neuroprognosis in Intensive Care’. In: Anaesthesia Critical Care & Pain
Medicine (Sept. 2023), p. 101301. DOI: 10.1016/j.accpm.2023.101301. URL: https://hal.sor
bonne-universite.fr/hal-04227434.

[24] Y. Thanwerdas and X. Pennec. ‘Bures-Wasserstein minimizing geodesics between covariance
matrices of different ranks’. In: SIAM Journal on Matrix Analysis and Applications 44.3 (30th Sept.
2023), pp. 1447–1476. DOI: 10.1137/22M149168X. URL: https://hal.science/hal-03647321.

[25] Y. Thanwerdas and X. Pennec. ‘O(n)-invariant Riemannian metrics on SPD matrices’. In: Linear
Algebra and its Applications 661 (Mar. 2023), pp. 163–201. DOI: 10.1016/j.laa.2022.12.009.
URL: https://hal.science/hal-03338601.

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[26] P. Tourniaire, M. Ilie, P. Hofman, N. Ayache and H. Delingette. ‘MS-CLAM: Mixed Supervision for
the classification and localization of tumors in Whole Slide Images’. In: Medical Image Analysis 85
(2023), p. 102763. DOI: 10.1016/j.media.2023.102763. URL: https://inria.hal.science/h
al-03972289.

[27] Z. Wang, Z. Xu, J. He, H. Delingette and J. Fan. ‘Long Short-Term Memory Neural Equalizer’. In:
IEEE Transactions on Signal and Power Integrity 2 (2023), pp. 13–22. DOI: 10.1109/TSIPI.2023.3
242855. URL: https://inria.hal.science/hal-04042921.

International peer-reviewed conferences

[28] M. Akhøj, X. Pennec and S. Sommer. ‘Tangent phylogenetic PCA’. In: Scandinavian Conference
on Image Analysis 2023. Vol. 13886. Lecture Notes in Computer Science. Levi Ski Resort (Lapland),
Finland: Springer Nature Switzerland, 2022, pp. 77–90. DOI: 10.1007/978-3-031-31438-4_6.
URL: https://inria.hal.science/hal-03842847.

[29]

S. Al-Ali, J. Llopis-Lorente, M. Teresa Mora, M. Sermesant, B. Trénor and I. Balelli. ‘A causal discovery
approach for streamline ion channels selection to improve drug-induced TdP risk assessment’. In:
IEEE Xplore. 2023 Computing in Cardiology (CinC). 2023 Computing in Cardiology (CinC). Atlanta
(GA), United States, 26th Dec. 2023. DOI: 10.22489/CinC.2023.009. URL: https://hal.scienc
e/hal-04105144.

[30] A. Calissano, E. Maignant and X. Pennec. ‘Towards Quotient Barycentric Subspaces’. In: Lecture
Notes in Computer Science. GSI 2023: Geometric Science of Information. Vol. 14071. Lecture Notes
in Computer Science. Saint-Malo (France), France: Springer Nature Switzerland, 1st Aug. 2023,
pp. 366–374. DOI: 10.1007/978-3-031-38271-0_36. URL: https://inria.hal.science/hal
-04162647.

[31] N. Cedilnik and J.-M. Peyrat. ‘Weighted tissue thickness’. In: Functional Imaging and Modeling of
the Heart 2023. FIMH 2023 - 12th International Conference On Functional Imaging And Modeling
Of The Heart. Lecture Notes in Computer Science. Lyon, France: Springer, 19th June 2023. URL:
https://inria.hal.science/hal-04111276.

[32] H. Dadoun, H. Delingette, A.-L. Rousseau, E. de Kerviler and N. Ayache. ‘Joint representation
learning from french radiological reports and ultrasound images’. In: IEEE ISBI 2023 - International
Symposium on Biomedical Imaging. Cartagena de Indias, Colombia, 18th Apr. 2023. URL: https:
//inria.hal.science/hal-03984528.

[33] Y. Fraboni, L. Innocenti, M. Antonelli, R. Vidal, L. Kameni, S. Ourselin and M. Lorenzi. ‘Valida-
tion of Federated Unlearning on Collaborative Prostate Segmentation’. In: DECAF MICCAI 2023
Workshops. Vol. 14393. Lecture Notes in Computer Science. Toronto, Canada: Springer Nature
Switzerland, 1st Dec. 2023, pp. 322–333. DOI: 10.1007/978-3-031-47401-9_31. URL: https:
//inria.hal.science/hal-04417106.

[34] E. Maignant, A. Trouvé and X. Pennec. ‘Riemannian Locally Linear Embedding with Application
to Kendall Shape Spaces’. In: Lecture Notes in Computer Science. GSI 2023: Geometric Science of
Information. Vol. 14071. Lecture Notes in Computer Science. Saint-Malo, (France), France: Springer
Nature Switzerland, 1st Aug. 2023, pp. 12–20. DOI: 10 . 1007 / 978 - 3 - 031 - 38271 - 0 _ 2. URL:
https://inria.hal.science/hal-04122754.

[35] T. Soulier, M. Hamzaoui, M. Sales Pitombeira, D. De Paula Faria, A. Yazdan-Panah, M. Tonietto,
C. Leroy, M. Bottlaender, B. Bodini, N. Burgos, N. Ayache, O. Colliot and B. Stankoff. ‘Generating
PET-derived maps of myelin content from clinical MRI using curricular discriminator training in
generative adversarial networks’. In: SPIE Medical Imaging. San Diego, United States, 20th Feb.
2024. URL: https://hal.sorbonne-universite.fr/hal-04362506.

[36] H. Sreedhar, G. P. R. Lajoinie, C. Raffaelli and H. Delingette. ‘Active Learning Strategies on a Real-
World Thyroid Ultrasound Dataset’. In: LNCS. Lecture Notes in Computer Science. DALI 2023 - Data
Augmentation, Labelling, and Imperfections / MICCAI Workshop 2023. MICCAI Workshop on Data
Augmentation, Labelling, and Imperfections, DALI 2023, held in conjunction with MICCAI 2023
Proceedings. Vancouver, Canada, 2023. URL: https://inria.hal.science/hal-04209622.

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[37] T. Szwagier and X. Pennec. ‘Rethinking the Riemannian Logarithm on Flag Manifolds as an Ortho-
gonal Alignment Problem’. In: Lecture Notes in Computer Science. GSI 2023: Geometric Science of
Information. Vol. 14071. Lecture Notes in Computer Science. Saint-Malo, (France), France: Springer
Nature Switzerland, 1st Aug. 2023, pp. 375–383. DOI: 10.1007/978-3-031-38271-0_37. URL:
https://inria.hal.science/hal-04100534.

[38] Y. Thanwerdas and X. Pennec. ‘Characterization of Invariant Inner Products’. In: Lecture Notes
in Computer Science. GSI 2023 - International Conference on Geometric Science of Information.
Vol. LNCS-14071. Geometric Science of Information: 6th International Conference, GSI 2023, St.
Malo, France, August 30 – September 1, 2023, Proceedings, Part I. Saint Malo, France, France:
Springer Nature Switzerland, 1st Aug. 2023, pp. 384–391. DOI: 10.1007/978-3-031-38271-0_38.
URL: https://inria.hal.science/hal-04229093.

[39] Y. Yang and M. Sermesant. ‘Unsupervised Polyaffine Transformation Learning for Echocardiography
Motion Estimation’. In: FIMH 2023 - The 12th International Conference on Functional Imaging
and Modeling of The Heart. Lyon, France, 19th June 2023. URL: https://inria.hal.science/h
al-04109721.

Conferences without proceedings

[40] Z. Fang, H. Delingette and N. Ayache. ‘Anatomical Landmark Detection for Initializing US and
MR Image Registration’. In: MICCAI ASMUS 2023 - 4th International Workshop of Advances in
Simplifying Medical UltraSound - a workshop held in conjunction with MICCAI 2023, the 26th
International Conference on Medical Image Computing and Computer Assisted Intervention.
Vancouver, Canada, 8th Oct. 2023. URL: https://hal.science/hal-04189905.

[41] E. Haxholli and M. Lorenzi. ‘Enhanced Distribution Modelling via Augmented Architectures For
Neural ODE Flows’. In: DLDE-III Workshop in the 37th Conference on Neural Information Pro-
cessing Systems (NeurIPS 2023). New Orleans, Louisiana, United States, 15th Dec. 2022. URL:
https://inria.hal.science/hal-03911870.

[42] E. Haxholli and M. Lorenzi. ‘Faster Training of Diffusion Models and Improved Density Estima-
tion via Parallel Score Matching’. In: NeurIPS 2023 Workshop on Diffusion Models. New Orleans,
Louisiana, United States, 10th Dec. 2023. URL: https://inria.hal.science/hal-04032669.

[43] L. Innocenti, M. Antonelli, F. Cremonesi, K. Sarhan, A. Granados, V. Goh, S. Ourselin and M. Lorenzi.
‘Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation’.
In: ECML - PharML - Applications of Machine Learning in Pharma and Healthcare (Workshop
at ECML PKDD 2023). Turin (IT), Italy: arXiv, 2023. DOI: 10.48550/arXiv.2309.17097. URL:
https://inria.hal.science/hal-04357349.

Scientific book chapters

[44] M. Lorenzi, M. Deprez, I. Balelli, A. L. Aguila and A. Altmann. ‘Integration of Multimodal Data’. In:
Machine Learning for Brain Disorders. Vol. NM197. Neuromethods. Springer, 2023, pp. 573–597.
DOI: 10.1007/978-1-0716-3195-9_19. URL: https://hal.science/hal-04239814.

[45] B. Ly, M. Pop, H. Cochet, N. Duchateau, D. O’regan and M. Sermesant. ‘Outcome Prediction’. In: AI
and Big Data in Cardiology : A Practical Guide. Springer International Publishing, 5th May 2023,
pp. 105–133. DOI: 10.1007/978-3-031-05071-8_6. URL: https://hal.science/hal-042120
68.

Doctoral dissertations and habilitation theses

[46] G. Desrues. ‘Personalised 3D electromechanical models of the heart for cardiac resynchronisation
therapy planning in heart failure patients’. Université Côte d’Azur, 28th Mar. 2023. URL: https://t
heses.hal.science/tel-04220830.

[47] Y. Fraboni. ‘Reliability and robustness of federated learning in practical applications’. Université

Côte d’Azur, 11th May 2023. URL: https://theses.hal.science/tel-04141520.

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[48] D. Hamzaoui. ‘AI-based diagnosis of prostate cancer from multiparametric MRI’. Université Côte

d’Azur, 26th June 2023. URL: https://hal.science/tel-04166332.

[49] E. Haxholli. ‘Scalable and flexible density estimation for complex data distributions’. Université

Côte d’Azur, 25th Oct. 2023. URL: https://theses.hal.science/tel-04416188.

[50] V. Kashtanova. ‘Learning cardiac electrophysiology dynamics with PDE-based physiological con-
straints for data-driven personalised predictions’. Université Côte d’Azur, 14th June 2023. URL:
https://hal.science/tel-04176798.

[51] E. Maignant. ‘Barycentric embeddings for geometric manifold learning : with application to shapes
and graphs’. Université Côte d’Azur, 6th Dec. 2023. URL: https://theses.hal.science/tel-0
4452790.

[52] M. A. Pedersen. ‘Riemannian and sub-Riemannian methods for dimension reduction’. INRIA
Sophia-Antipolis; University of Copenhagen, 23rd Nov. 2023. URL: https://theses.hal.scienc
e/tel-04391602.

[53]

S. S. Silva Rincon. ‘Federated Learning of Biomedical Data in Multicentric Imaging Studies’. UCA,
Inria, 18th July 2023. URL: https://inria.hal.science/tel-04417044.

[54] P. Tourniaire. ‘AI-based selection of imaging and biological markers predictive of therapy response
in lung cancer’. Université Côte d’Azur, 12th June 2023. URL: https://theses.hal.science/te
l-04189450.

[55] Y. Yang. ‘Automatic analysis of cardiac function with artificial intelligence: multimodal approach
for portable echocardiographic devices’. Université Côte d’Azur; Centre Inria d’Université Côte
d’Azur, 19th Dec. 2023. URL: https://inria.hal.science/tel-04422777.

Reports & preprints

[56] Y. Aeschlimann, A. Calissano, A. Abildtrup Hansen, T. Papadopoulo and S. Deslauriers-Gauthier.
Graph matching of structural and functional brain networks. 11th Jan. 2024. URL: https://hal.s
cience/hal-04387986.

[57] M. Akhøj, J. Benn, E. Grong, S. Sommer and X. Pennec. Principal subbundles for dimension reduction.

6th July 2023. URL: https://inria.hal.science/hal-04156036.

[58]

I. Balelli, A. Sportisse, F. Cremonesi, P.-A. Mattei and M. Lorenzi. Fed-MIWAE: Federated Imputation
of Incomplete Data via Deep Generative Models. 2023. URL: https://hal.science/hal-0406979
5.

[59]

J. Benn and S. Marsland. Towards a BCH Formula on the Diffeomorphism Group with a Right-
Invariant Metric. 22nd Dec. 2023. URL: https://hal.science/hal-04360903.

[60] F. Blanqui, A. Canteaut, H. de Jong, S. Imperiale, N. Mitton, G. Pallez, X. Pennec, X. Rival and B.
Thirion. Recommandations sur les « éditeurs de la zone grise ». Inria, 25th Jan. 2023, pp. 1–3. URL:
https://inria.hal.science/hal-04001505.

[61] F. Blanqui, A. Canteaut, H. D. Jong, S. Imperiale, N. Mitton, G. Pallez, X. Pennec, X. Rival and B.
Thirion. Recommendations on "Grey-Zone Publishers": Recommendations from the Inria Evaluation
Committee, translated from h t t p s : // h a l . i n r i a . f r / h a l -0 4 0 0 1 5 0 5 . Inria, 25th Jan.
2023, pp. 1–3. URL: https://inria.hal.science/hal-04201298.

[62] B. Buet and X. Pennec. Flagfolds. 17th May 2023. URL: https://inria.hal.science/hal-0411

2465.

[63] F. Cremonesi, M. Vesin, S. Cansiz, Y. Bouillard, I. Balelli, L. Innocenti, S. Silva, S.-S. Ayed, R. Taiello,
L. Kameni, R. Vidal, F. Orlhac, C. Nioche, N. Lapel, B. Houis, R. Modzelewski, O. Humbert, M. Önen
and M. Lorenzi. Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world
Healthcare Applications. 25th Apr. 2023. URL: https://inria.hal.science/hal-04081557.

[64] Y. Fraboni, R. Vidal, L. Kameni and M. Lorenzi. Sequential Informed Federated Unlearning: Efficient
and Provable Client Unlearning in Federated Optimization. 2nd Jan. 2023. URL: https://hal.sci
ence/hal-03910848.

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[65] D. Rabenoro and X. Pennec. The geometry of Riemannian submersions from compact Lie groups.
Application to flag manifolds. 1st Mar. 2023. URL: https://inria.hal.science/hal-04232479.

[66] T. Szwagier and X. Pennec. Stratified principal component analysis. 2nd Nov. 2023. URL: https://i

nria.hal.science/hal-04171853.

[67] Y. Thanwerdas. Permutation-invariant log-Euclidean geometries on full-rank correlation matrices.

24th Nov. 2023. URL: https://hal.science/hal-03878729.

Other scientific publications

[68] A. Abildtrup Hansen, Y. Aeschlimann, S. Deslauriers-Gauthier and A. Calissano. ‘Permutation
equivariant structure-function mapping’. In: 2023 Annual Meeting of the Organization for Human
Brain Mapping. Montreal (Canada), Canada, 22nd July 2023. URL: https://hal.science/hal-0
4390750.

[69] Y. Aeschlimann, A. Calissano, S. Deslauriers-Gauthier, T. Papadopoulo and A. Hansen. ‘Preliminary
results on the graph matching of structural and functional brain networks’. In: Neuromod Yearly
Meeting 2023. Antibes, France, 28th June 2023. URL: https://inria.hal.science/hal-041652
09.

[70] A. Calissano, T. Papadopoulo, X. Pennec and S. Deslauriers-Gauthier. ‘Graph Matching improves the
Similarity of Structural Brain Networks’. In: OHBM 2023 - Organization for Human Brain Mapping.
Montreal (Canada), Canada, 22nd July 2023. URL: https://hal.science/hal-04390489.
<scratchpad>
- Main topic: Research Centre EPIONE at Inria Centre at Université Côte d’Azur
- Key points:
  - Research objectives organized along 5 scientific axes: Biomedical Image Analysis & Machine Learning, Imaging & Phenomics, Computational Anatomy, Computational Physiology, and Computational Cardiology
  - Development of personalized models for e-patients and e-medicine
  - Applications in digital health, biology, and earth sciences
  - Focus on computational neuroscience and medicine
  - Research program includes Biomedical Image Analysis & Machine Learning, Imaging & Phenomics, Computational Anatomy, Computational Physiology, and Computational Cardiology
- Structure: The document is organized into sections, including Activity Report, Project-Team, DOMAIN, THEME, Contents, Research program, Application domains, Social and environmental responsibility, Highlights of the year, New software, platforms, open data, New results, Bilateral contracts and grants with industry, Partnerships and cooperations, Dissemination, Scientific production, and more
- Potential artifacts to ignore: Unnecessary web-related elements, such as links, HTML tags, and other web-related elements
</scratchpad>

<final_summary>
The EPIONE research centre at Inria Centre at Université Côte d’Azur focuses on developing personalized models for e-patients and e-medicine, with a strong emphasis on computational neuroscience and medicine. The centre's research program is organized into five scientific axes, including Biomedical Image Analysis & Machine Learning, Imaging & Phenomics, Computational Anatomy, Computational Physiology, and Computational Cardiology. The centre's work has applications in digital health, biology, and earth sciences, and aims to improve patient outcomes and healthcare services. The centre's research has been recognized with several awards, including the Prix de l’innovation Académie des Sciences - Inria - Dassault Systèmes.
The EPIONE research centre at Inria Centre at Université Côte d’Azur focuses on developing personalized models for e-patients and e-medicine, with a strong emphasis on computational neuroscience and medicine. The centre's research program is organized into five scientific axes, including Biomedical Image Analysis & Machine Learning, Imaging & Phenomics, Computational Anatomy, Computational Physiology, and Computational Cardiology. The centre's work has applications in digital health, biology, and earth sciences, and aims to improve patient outcomes and healthcare services. The centre's research has been recognized with several awards, including the Prix de l’innovation Académie des Sciences - Inria - Dassault Systèmes.