RESEARCH CENTRE

Inria Saclay Centre
at Université Paris-Saclay

IN PARTNERSHIP WITH:

Université Paris-Saclay

2023
ACTIVITY REPORT

Project-Team

OPIS

OPtImization for large Scale biomedical
data

DOMAIN

Digital Health, Biology and Earth

THEME

Computational Neuroscience and
Medicine

Contents

Project-Team OPIS

1 Team members, visitors, external collaborators

2 Overall objectives

3 Research program

3.1 Accelerated algorithms for solving high-dimensional continuous optimization problems .
3.2 Optimization over graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Toward more understandable deep learning . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

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4 Application domains
4.1 Robustness of AI

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.1 Design of robust neural networks in safety critical industrial domains . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.2 Certification of Segmentation Networks
4.2 Radiology, hyper-progressive disease and immunotherapy . . . . . . . . . . . . . . . . . . .
. . . . . . . . . .
4.2.1 Imaging radiomics and pathomics to assess response to treatment
4.2.2 Analysis of histopathology images for cancer treatment
. . . . . . . . . . . . . . . . .
4.2.3 Vision, machine learning and precision medicine . . . . . . . . . . . . . . . . . . . . .
4.2.4 Physics-informed, generative models for heart left ventricle perfusion analysis . . .
4.3 Sparse inverse problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.1 Sparse signal processing in chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.2 Image restoration for multiphoton microscopy . . . . . . . . . . . . . . . . . . . . . .
4.3.3 Reconstruction approaches in interventional CT imaging . . . . . . . . . . . . . . . .
4.3.4 Reconstruction approaches in PET imaging . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Graph mining applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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4.4.1 Graph inference for gene regulation analysis . . . . . . . . . . . . . . . . . . . . . . . . 10
4.4.2 Graph machine learning for spatiotemporal data . . . . . . . . . . . . . . . . . . . . . 10
4.4.3 Graph representation learning for computational medicine . . . . . . . . . . . . . . . 10
4.4.4 Graph inference for time series analysis in earth and climate applications . . . . . . 11
4.5 Other biomedical applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.5.1 Imaging biomarkers and characterization for chronic lung diseases . . . . . . . . . . 11
4.5.2 AI for small bowel obstruction diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.5.3 A generative model for heart left ventricle perfusion analysis . . . . . . . . . . . . . . 12

5 Social and environmental responsibility

12
5.1 Footprint of research activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5.2 Impact of research results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

6 Highlights of the year

13
6.1 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

7 New software, platforms, open data

13
7.1 New platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.1.1 Prox Repository . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.1.2 The PINK image library . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
7.1.3 The Vivabrain AngioTK toolkit
7.1.4 A scientific image viewer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
7.1.5 TCGA segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
7.1.6 ScanCovIA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
7.1.7 Graphical inference in linear-Gaussian state-space models . . . . . . . . . . . . . . . 15
7.1.8 Matrix factorization for drug discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
7.1.9 Joint registration tumor segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
7.1.10 PMCNet
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
7.1.11 U-HQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

7.1.12 FAENet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
7.1.13 SJLR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
7.1.14 GLIE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

8 New results

18
8.1 Theoretical and practical advances for majorize-minimize algorithms . . . . . . . . . . . . . 18
8.2 Fundamental problems in image reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . 18
8.3 Deep unrolled algorithms for inverse problems in image and signal processing . . . . . . . 18
8.4 Computational approaches for multiphotonic image restoration . . . . . . . . . . . . . . . . 19
8.5 Monte-Carlo approaches for global optimization . . . . . . . . . . . . . . . . . . . . . . . . . 19
8.6 Fast schemes for adaptive importance sampling . . . . . . . . . . . . . . . . . . . . . . . . . . 19
8.7 Computational approaches for drug discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
8.8 Expectation-Maximization for time series modeling and inference . . . . . . . . . . . . . . . 20
8.9 Advanced nonconvex priors for signal and image restoration . . . . . . . . . . . . . . . . . . 21
8.10 Interpretable ensembling rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
8.11 Novel joint learning approaches for neural networks based multiscale transforms . . . . . . 21
8.12 Designing robust neural networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
8.13 Neural speed-torque estimator for induction motors . . . . . . . . . . . . . . . . . . . . . . . 22
8.14 Proximal splitting adversarial attack for semantic segmentation . . . . . . . . . . . . . . . . 22
8.15 Artificial intelligence for radiology and medicine, with application to immunotherapy . . . 23
8.16 Interpretable deep learning via binary morphological neural networks . . . . . . . . . . . . 23
8.17 Deep generative method for musculo-skeletal imaging . . . . . . . . . . . . . . . . . . . . . . 23
8.18 Whole-slide pathology analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
8.19 Certification of Segmentation Networks using Diffusion Models . . . . . . . . . . . . . . . . 24
8.20 Computational Pathology and Foundation Models . . . . . . . . . . . . . . . . . . . . . . . . 24
. . . . . . . . . . . . . . . . . . 25
8.21 Large Language Models for prognosis of the renal transplant
8.22 Differentiating Nonsmooth Solutions to Parametric Monotone Inclusion Problems . . . . . 25
8.23 Geometric GNNs for materials modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
8.24 Spatio-temporal data analysis with GNNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
8.25 Towards deep GNN architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
8.26 Higher-order sparse convolutions in GNNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
8.27 Influence maximization with GNNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

9 Bilateral contracts and grants with industry

27
9.1 Bilateral contracts with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

10 Partnerships and cooperations

10.2 International research visitors

27
10.1 International initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
10.1.1 Inria associate team not involved in an IIL or an international program . . . . . . . . 27
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
10.2.1 Visits of international scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
10.2.2 Visits to international teams
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
10.4 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
10.4.1 ANR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
10.4.2 Others

10.3 European initiatives

10.3.1 H2020 projects

11 Dissemination

32
11.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
11.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
11.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
11.1.3 Journal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
11.1.4 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
11.1.5 Leadership within the scientific community . . . . . . . . . . . . . . . . . . . . . . . . 35
11.1.6 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

11.1.7 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
11.1.8 Teaching administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
11.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
11.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
11.2.2 PhD supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
11.2.3 Intern supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
11.2.4 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
11.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
11.3.1 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

12 Scientific production

12.1 Publications of the year

41
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

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Project-Team OPIS

Creation of the Project-Team: 2019 May 01

Keywords

Computer sciences and digital sciences

A3.4. – Machine learning and statistics

A3.4.1. – Supervised learning

A3.4.2. – Unsupervised learning

A3.4.3. – Reinforcement learning

A3.4.4. – Optimization and learning

A3.4.5. – Bayesian methods

A3.4.6. – Neural networks

A3.4.8. – Deep learning

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

A6.2.4. – Statistical methods

A6.2.6. – Optimization

A8.2. – Optimization

A8.7. – Graph theory

A9.2. – Machine learning

A9.3. – Signal analysis

A9.7. – AI algorithmics

Other research topics and application domains

B1. – Life sciences

B1.1. – Biology

B1.2. – Neuroscience and cognitive science

B2.6. – Biological and medical imaging

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Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientist

• Emilie Chouzenoux [Team leader, INRIA, Senior Researcher, HDR]

Faculty Members

• Mounir Kaaniche [UNIV PARIS XIII, Associate Professor, until Aug 2023, DELEGATION INRIA]

• Fragkiskos Malliaros [CENTRALESUPELEC, Associate Professor]

• Nora Ouzir [CENTRALESUPELEC, Associate Professor]

• Jean-Christophe Pesquet [CENTRALESUPELEC, Professor, HDR]

• Tony Silveti-Falls [CENTRALESUPELEC, Associate Professor]

• Hugues Talbot [CENTRALESUPELEC, Professor, HDR]

• Maria Vakalopoulou [CENTRALESUPELEC, Associate Professor]

Post-Doctoral Fellow

• Fernando Marcelo Roldan Contreras [INRIA, Post-Doctoral Fellow, until Nov 2023]

PhD Students

• Yassine Abbahaddou [ECOLE POLYTECHNIQUE]

• Theodore Aouad [CENTRALESUPELEC]

• Younes Belkouchi [CENTRALESUPELEC]

• Alix Chazottes [CENTRALESUPELEC, from Dec 2023]

• Clement Cosserat [INRIA]

• Tassnim Dardouri [UNIVERSITE PARIS XIII, until Feb 2023]

• Alexandre Duval [CENTRALESUPELEC]

• Raaja El Hamdani [TELECOM PARISTECH]

• Hafsa El Herichi [CENTRALESUPELEC, CIFRE, from Mar 2023, CIFRE PHD - SAFRAN]

• Jean-Baptiste Fest [INRIA, until Aug 2023]

• Mouna Gharbi [CENTRALESUPELEC]

• Thomas Guilmeau [INRIA]

• Kavya Gupta [CENTRALESUPELEC, CIFRE, until Jan 2023, CIFRE PHD - THALES]

• Othmane Laousy [CENTRALESUPELEC, until Mar 2023]

• Loïc Le Bescond [CENTRALESUPELEC]

• Ségolène Martin [CENTRALESUPELEC, until Nov 2023]

• Vahan Martirosyan [CENTRALESUPELEC, from Nov 2023]

• Léo Milecki [CENTRALESUPELEC, until Feb 2023]

Project OPIS

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• Ana Neacsu [CENTRALESUPELEC]

• Nicolas Salvy [INRIA, from Dec 2023]

• Aymen Sardroui [CENTRALESUPELEC]

• Gabriele Scrivanti [CENTRALESUPELEC]

• Sagar Verma [CENTRALESUPELEC, CIFRE, until Jun 2023, CIFRE PHD - SCHNEIDER]

• Nguyen Vu [INRIA]

Technical Staff

• Ezequiel Tomas Centofanti [INRIA, Engineer, until Feb 2023]

• Yunshi Huang [INRIA, Engineer, until Feb 2023]

• Caglayan Tuna [INRIA, Engineer]

Interns and Apprentices

• Eliott Barbot [CENTRALESUPELEC, Intern, from May 2023 until Oct 2023]

• Théau Blanchard [INSERM, Intern, from Apr 2023 until Oct 2023]

• Alix Chazottes [Hôpital St Joseph, Intern, from Apr 2023 until Oct 2023]

• Nicolas Dunou [CENTRALESUPELEC, Intern, from May 2023 until Sep 2023]

• Jean-Luc Dupire [CENTRALESUPELEC, Intern, from Jun 2023 until Aug 2023]

• Roxane Housny [CENTRALESUPELEC, Intern, from Apr 2023 until Jun 2023]

• Vuk Ignjatovic [CENTRALESUPELEC, Intern, from Mar 2023 until Sep 2023]

• Mahuton Hugues Midingoyi [INRIA, Intern, from Apr 2023 until Aug 2023]

• Corentin Pla [CENTRALESUPELEC, Intern, from Jun 2023 until Oct 2023]

• Antoine Siraudin [CENTRALESUPELEC, Intern, from Jun 2023 until Nov 2023]

• Léo Stepien [CENTRALESUPELEC, Intern, from Jun 2023 until Nov 2023]

Administrative Assistants

• Jana Dutrey [CENTRALESUPELEC, Financial and administrative gestion of CVN]

• Joyce Soares Brito [INRIA, AER OPIS]

Visiting Scientists

• Amel Benazza Benyahia [SUPCOM Tunis, from Jun 2023 until Jul 2023]

• Hamid Krim [CENTRALESUPELEC, from May 2023 until Jun 2023]

• Angshul Majumdar [IIIT Delhi, from May 2023 until Jun 2023]

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Inria Annual Report 2023

2 Overall objectives

Mathematical optimization is the key to solving many problems in science, based on the observation that
physical systems obey a general principle of least action. While some problems can be solved analytically,
many more can only be solved via numerical algorithms. Research in this domain has been steadily
ongoing for decades.

In addition, many fields such as medecine continue to benefit from considerable improvements in
data acquisition technology, based on sophisticated tools from optics and physics (e.g., new laser sources
in microscopy, multi-coil systems in MRI, novel X-ray schemes in mammography, etc). This evolution is
expected to yield significant improvements in terms of data resolution, making the interpretation and
analysis of the results easier and more accurate for the practitioner. The large amounts of generated
data must be analyzed by sophisticated optimization tools so that, in recent years, optimization has
become a main driving force fostering significant advances in data processing. Previously hidden or hard
to extract information can be pried from massive datasets by modern recovery and data mining methods.
At the same time, automated decision and computer-aided diagnoses are made possible through optimal
learning approaches.

However, major bottlenecks still exist. Recent advances in instrumentation techniques come with
the need to minimize functions involving an increasingly large number of variables (at least one billion
variables in 3D digital tomography modality), and with increasingly complex mathematical structure.
The computational load for solving these problems may be too high for even state-of-the-art algorithms.
New algorithms must be designed with computational scalability, robustness, and versatility in mind.
In particular, the following severe requirements must be fulfilled: (i) ability to tackle high-dimensional
problems in a reasonable computation time; (ii) low-requirements in terms of memory usage; (iii)
robustness to incomplete or unreliable information; (iv) adaptivity to statistically varying environments;
(v) resilience to latency issues arising in architectures involving multiple computing units.

These difficulties are compounded in the medical and biomedical areas. In these contexts, datasets
are not easily available due to patient confidentiality and/or instrument limitations. Moreover, high-level
expertise is necessary to interpret the data which can be of very high dimension. Finally, the developed
analysis methods must be reliable and interpretable by the medical/biomedical community.

The objective of the OPIS project is to design advanced optimization methods for the analysis and
processing of large and complex data. Applications to inverse problems and machine learning tasks in
biomedical imaging are major outcomes of this research project. We seek optimization methods able
to tackle data with both a large sample-size (“big N " e.g., N = 109) and/or many measurements (“big
P " e.g., P = 104). The methodologies to be explored are grounded on nonsmooth functional analysis,
fixed point theory, parallel/distributed strategies, and neural networks. The new optimization tools that
are developed are set in the general framework of graph signal processing, encompassing both regular
graphs (e.g., images) and non-regular graphs (e.g., gene regulatory networks).
More specifically, three main research avenues are explored, namely:

1. Accelerated algorithms for solving high-dimensional continuous optimization problems ;

2. Optimization over graphs ;

3. Toward more understandable deep learning.

In summary, the specificity of OPIS is to address problems involving high-dimensional biomedical data,
e.g. 3D CT, PET, ultrasound images, and MRI, by making use of advanced computational optimization
methods.

3 Research program

3.1 Accelerated algorithms for solving high-dimensional continuous optimization

problems

Variational problems requiring the estimation of a huge number of variables have now to be tackled,
especially in the field of 3D reconstruction/restoration (e.g. ≥ 109 variables in 3D imaging). In addition

Project OPIS

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to the curse of dimensionality, another difficulty to overcome is that the cost function usually reads as
the sum of several loss/regularization terms, possibly composed with large-size linear operators. These
terms can be nonsmooth and/or nonconvex, as they may serve to promote the sparsity of the sought
solution in some suitable representation (e.g. a frame) or to fufill some physical constraints. In such
a challenging context, there is a strong need for developing fast parallelized optimization agorithms
for which sound theoretical guarantees of convergence can be established. We explore deterministic
and stochastic approaches based on proximal tools, MM (Majorization-Minimization) strategies, and
trust region methods. More generally, we are interested in using fixed point methods which provide
a simplifying and unifying framework to model, analyze, and solve a great variety of problems. They
constitute a natural environment to explain the behavior of advanced convex optimization methods as
well as of recent nonlinear methods in data science which are formulated in terms of paradigms that go
beyond minimization concepts and involve constructs such as Nash equilibria or monotone inclusions.
Because of the versatility of the methods we work on, a wide range of applications in image recovery are
considered: parallel MRI, breast tomosynthesis, 3D ultrasound imaging, and two-photon microscopy.
For example, in breast tomosynthesis (collaboration with GE Healthcare), 3D breast images have to be
reconstructed from a small number of X-ray projections with limited view angles. Our objective is to
facilitate the clinical task by developing advanced reconstruction methods allowing micro-calcifications
to be highlighted. In two-photon microscopy (collaboration with XLIM), our objective is to provide
effective numerical solutions to improve the 3D resolution of the microscope, especially when cheap
laser sources are used, with applications to muscle disease screening.

3.2 Optimization over graphs

Graphs and hypergraphs are rich data structures for capturing complex, possibly irregular, dependen-
cies in multidimensional data. Coupled with Markov models, they constitute the backbones of many
techniques used in computer vision. Optimization is omnipresent in graph processing. Firstly, it allows
the structure of the underlying graph to be inferred from the observed data, when the former is hidden.
Second, it permits to develop graphical models based on the prior definition of a meaningful cost function.
This leads to powerful nonlinear estimates of variables corresponding to unknown weights on the vertices
and/or the edges of the graph. Tasks such as partitioning the graph into subgraphs corresponding to
different clusters (e.g., communities in social networks) or graph matching, can effectively be performed
within this framework. Finally, graphs by themselves offer flexible structures for formulating and solving
optimization problems in an efficient distributed manner. On all these topics, our group has acquired
a long-term expertise that we plan to further strengthen. In terms of applications, novel graph mining
methods are proposed for gene regulatory and brain network analysis. For example, we plan to develop
sophisticated methods for better understanding the gene regulatory network of various microscopic
fungi, in order to improve the efficiency of the production of bio-fuels (collaboration with IFP Energies
Nouvelles).

3.3 Toward more understandable deep learning

Nowadays, deep learning techniques efficiently solve supervised tasks in classification or regression
by utilizing large amounts of labeled data and the powerful high level features that they learn by using
the input data. Their good performance has caught the attention of the optimization community since
currently these methods offer virtually no guarantee of convergence, stability or generalization. Deep
neural networks are optimized through a computationally intensive engineering process via methods
based on stochastic gradient descent. These methods are slow and they may not lead to relevant local
minima. Thus, more efforts must be dedicated in order to improve the training of deep neural networks
by proposing better optimization algorithms applicable to large-scale datasets. Beyond optimization,
incorporating some structure in deep neural networks permits more advanced regularization than
the current methods. This should reduce their complexity, as well as allow us to derive some bounds
regarding generalization. For example, many signal processing models (e.g. those based on multiscale
decompositions) exhibit some strong correspondence with deep learning architectures, yet they do not
require as many parameters. One can thus think of introducing some supervision into these models in
order to improve their performance on standard benchmarks. A better mathematical understanding of

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Inria Annual Report 2023

these methods permits to improve them, but also to propose some new models and representations for
high-dimensional data. This is particularly interesting in settings such as the diagnosis or prevention
of diseases from medical images, because they correspond to critical applications where the made
decision is crucial and needs to be interpretable. One of the main applications of this work is to propose
robust models for the prediction of the outcome of cancer immunotherapy treatments from multiple and
complementary sources of information: images, gene expression data, patient profile, etc (collaboration
with Institut Gustave Roussy).

4 Application domains

4.1 Robustness of AI

4.1.1 Design of robust neural networks in safety critical industrial domains

Participants: Ana Neacsu, Kavya Gupta, Fragkiskos Malliaros, Jean-Christophe Pesquet (Collaboration: P.
Combettes, North Caroline State University ; C. Burileanu, Polithenica University of Bucharest)

One of the main challenges faced today by companies like Thales or Schneider Electric designing
advanced industrial systems, is to ensure the safety of new generations of products based on the use of
neural networks. Since 2013, neural networks have been shown to be sensitive to adversarial perturbations.
Deep neural networks can thus be fooled, in an intentional (security issue) or in undeliberate manner
(safety issue), which raises a major robustness concern for safety-critical systems which need to be
certified by an independent certification authority prior to any entry into production/operation. Tech-
niques based on mathematical proofs of robustness are generally preferred by industrial safety experts
since they enable a safe-by-design approach that is more efficient than a robustness verification activity
done a posteriori with a necessarily bounded effort. Among the possible mathematical approaches,
we focus on those relying upon the analysis of the Lipschitz properties of neural networks [26]. Such
properties play a fundamental role in the understanding of the internal mechanisms governing these
complex nonlinear systems. Besides, they make few assumptions on the type of non-linearities used and
are thus valid for a wide range of networks.

4.1.2 Certification of Segmentation Networks

Participants: Othmane Laousy, Maria Vakalopoulou (Collaboration: G. Chassagnon and M.-P. Revel, Paris
Cité University ; N. Paragios, Therapanacea ; A. Araujo, S. Garg and F. Khorram, New York University)

The robustness of image segmentation has been an important research topic in the past few years
as segmentation models have reached production-level accuracy. However, like classification models,
segmentation models can be vulnerable to adversarial perturbations, which hinders their use in critical-
decision systems like healthcare or autonomous driving. Recently, randomized smoothing has been
proposed to certify segmentation predictions by adding Gaussian noise to the input to obtain theoretical
guarantees. However, this method exhibits a trade-off between the amount of added noise and the level
of certification achieved. In this topic, we address the problem of certifying segmentation prediction
using a combination of randomized smoothing and diffusion models. We challenge our methods in both
general computer vision and medical imaging dataset.

4.2 Radiology, hyper-progressive disease and immunotherapy

4.2.1 Imaging radiomics and pathomics to assess response to treatment

Participants: Maria Vakalopoulou, Hugues Talbot (Collaboration: E. Deutsh, Institut Gustave Roussy ;
N. Paragios, Therapanacea)

The response of patients with cancer to immunotherapy can vary considerably, innovative predictors
of response to treatment are needed to improve treatment outcomes. We aimed to develop and independ-
ently validate radiomics-based biomarkers of tumour-infiltrating cells in patients included in trials of the
two most common, recent immunotherapy treatments: anti-programmed cell death protein (PD)-1 or

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anti-programmed cell death ligand 1 (PD-L1) monotherapy. We also aimed to evaluate the association
between the biomarker, and tumour immune phenotype and clinical outcomes of these patients.

However, sometimes, not only do patient respond poorly, but immunotherapy seems to make things
worse. Some patients see they tumoral load increase significantly faster after immunotherapy is started.
These patients are called “hyper-progressors”. One of our project has been to clearly define and detect this
class of patients. This is so novel that the very notion of hyperprogressive patient was still controversial
when our work was published, but is accepted now.

In this axis we investigate powerful representations for radiological and pathological data that could

be associated with interesting and important clinical questions.

4.2.2 Analysis of histopathology images for cancer treatment

Participants: Ségolène Martin, Nora Ouzir, Jean-Christophe Pesquet, Aymen Sardroui(Collaboration: A.
Laurent-Bellue, C. Guettier: APHP, Hôpital du Kremlin-Bicêtre ; A. Beaufrère, K. Mondet, V. Paradis, APHP,
Hôpital Beaujon)

The core focus of our research revolves around scrutinizing cancer through the utilization of digital
slide images resulting from biopsies or surgical resection. Our exploration stands at the intersection
of cutting-edge AI technology and its invaluable potential for advancing precision medicine, and more
particularly liver cancer (hepatocellular carcinoma and intrahepatic cholangiocarcinoma) diagnosis and
treatment. The challenges to be solved are related to the limited number of available annotated data and
the large-size of whole slide images (WSIs).

4.2.3 Vision, machine learning and precision medicine

Participants: Younes Belkouchi, Loïc Le Bescond, Hugues Talbot (Collaboration: N. Lassau, Institut
Gustave Roussy)

In March 2020, the PRISM institute of Gustave-Roussy was launched. The aim of this project, funded

for 5 years, is to develop targeted treatments that are more likely to work on specific patients.

The mission of this “second-generation” precision medicine centre will be to model cancer on an
individual scale by creating numerical avatars of tumours. The aim is to identify patients with the most
aggressive cancers very early in the disease, without waiting for relapses, in order to offer them the most
appropriate treatment from the start of treatment, using the huge volume of clinical, biological and
molecular data and their analysis by artificial intelligence. PRISM will conduct large-scale clinical studies
and develop molecular analysis technologies and data analysis methods.

Coordinated by Professor Fabrice André, Research Director of Gustave Roussy, Inserm Research
Director and Professor at Paris-Saclay University, Prism aims to revolutionize the understanding of
the molecular and biological mechanisms of cancer development and progression through artificial
intelligence. Based on increasingly rich data of various types (clinical, genomic, microbiological, imaging,
etc.), learning algorithms make it possible to develop finer diagnostic and prognostic tools, and thus to
propose therapies that are personalised according to the characteristics of the individual.

Funded by the French National Research Agency, PRISM received the IHU label in 2018, followed by

the National Center for Precision Medicine label.

4.2.4 Physics-informed, generative models for heart left ventricle perfusion analysis

Participants: Raoul Salle de Chou, Hugues Talbot (Collaboration: I. Vignon-Clementel, SIMBIOTX Team
Inria ; L. Najman, Université Gustave-Eiffel, L. Papamanolis Stanford university, USA, California)

Coronary arteries feed the heart muscles with nutrients and oxygen. As such, they are some of the
most critical blood vessel in the entire body. Coronary disease is difficult to diagnose especially when it
affects the smaller branches of these vessels, because direct imaging of these vessels is infeasible with
current medical imaging technology. Instead, blood perfusion through the myocardium can be imaged
and is correlated with both arterial and myocardium disease. However, perfusion imaging is challenging,
invasive and expensive because it relies on radioactive tracers.

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A previous model was developed for myocardial perfusion simulation for coronary artery disease in
[link] to replace the actual exam with a numerical twin and conduct it via simulations. The model aims at
reproducing [15O]H2O PET imaging exam using only CT scans as input. The simulation is based on :

1. the detection, segmentation and simulation of blood flow through the coronary vessels visible on

the injected CT scan;

2. a patient-specific method for generating small 3D vessels consistent with the vessels detected. The

rules for the growth of these vessels are based on physiology and simulated blood flows;

3. a perfusion simulation model that considers the myocardium as a porous medium.

For this a linear Darcy model is used to simulate blood flow through the porous medium. However,
in addition to a high computational cost, the simulation fails to accurately reproduce some diseases,
particularly those that affect medium-size coronary branches.

The main goal of this project is to combine Machine Learning (ML) methods with physical simulations,
in order to improve the current simulation pipeline. ML algorithms are used to learn from PET imaging
exams while being guided by simulation hypothesis, thereby diminishing the dependency on patient
data. To achieve this, each part of the simulation is to be replaced by an ML model. Following successful
replication of simulation outcomes, the model will undergoes refinement using patient data.

A finite volume physics informed graph neural network was developed to solve the Darcy equations
on irregular shapes serving as a substitute for the myocardium component in perfusion simulation.
Preliminary results indicate superior performance of this model in terms of accuracy and generalization
compared to classical ML approaches. In [54], we introduced a novel optimization framework for
the generation of the synthetic small vessels utilizing the constructed constrained optimization (CCO)
method. Our new approach simulated similar 2D vascular trees as the original CCO method in terms of
morphometry while producing better optimal solutions at lower computational cost. This new approach
is expected to be more readily reproducible using ML methods compared to the original CCO technique.
On this topic, an open-source CCO implementation was published on IPOL (Image Processing
OnLine) [22]. This has the potential to disseminate this useful method for vascular tree generation to a
wider audience.

Additionally, work has been conducted towards the determination of the myocardium perfusion
regions. Determining these regions, and their associated vessel is a crucial step in current simulation
pipeline. However, the current calculation method is inaccurate and highly sensitive to the resolution of
segmented vessels. A more robust and accurate model, employing graph neural networks (GNNs), has
been developed for the determination of these regions.

4.3 Sparse inverse problems

4.3.1 Sparse signal processing in chemistry

Participants: Emilie Chouzenoux, Paul Zheng (Collaboration: L. Duval, IFPEN, Rueil Malmaison)

Peak-signal retrieval is a core challenge in separative analytical chemistry (AC). For instance, in
chromatography, spectrometry, spectroscopy, peak localization, amplitude, width or area provide useful
chemical quantitative information. We investigated the problem of joint trend removal and blind de-
convolution of sparse peaklike signals. The trend accounts for mere offsets to slowly-varying amplitude
shifts (seasonality, calibration distortion, sensor decline), making its automated removal challenging. We
proposed the method PENDANTSS [30] that handles smooth trend removal by exploiting their low-pass
property and simplifies the problem into a blind deconvolution problem. The proposed approach im-
plements a convergent and efficient tool, with a novel Trust-Region block alternating variable metric
forward-backward approach. Simulation results confirm that PENDANTSS outperforms comparable
methods on typical sparse analytical signals. Collaboration with Dr. L. Duval, Research Engineer at IFP
Energies Nouvelles, France is on-going in this applicative context.

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4.3.2 Image restoration for multiphoton microscopy

Participants: Julien Adjenbaum, Emilie Chouzenoux, Jean-Baptiste Fest, Ségolène Martin, Jean-Christophe Pes-
quet (Collaboration: C. Lefort, XLIM, CNRS, Limoges ; M. Chalvidal, ANITI, Toulouse)

Through an ongoing collaboration with physicists from XLIM laboratory (CNRS, Limoges, France), we
propose advanced mathematical and computational solutions for multiphoton microscopy (MPM) 3D
image restoration. This modality enjoys many benefits such as a decrease in phototoxicity and increase
in penetration depth. However, blur and noise issues can be more severe than with standard confocal
images. Our objective is to drastically improve the quality of the generated images and their resolution
by improving the characterization of the PSF of the system and compensating its effect. We consider
the application of the improved MPM imaging tool to the microscopic analysis of muscle ultrastructure
and composition, with the aim to help diagnosing muscle disorders including rare and orphan muscle
pathologies [6], and to visualize bacteria and viral structures [13].

4.3.3 Reconstruction approaches in interventional CT imaging

Participants: Emilie Chouzenoux, Fernando Marcelo Roldan Contreras, Jean-Christophe Pesquet, Marion
Savanier (Collaboration: C. Riddel, Y. Trousset, GE Healthcare)

The objective of this collaboration with researchers from GE Healthcare, is to develop high quality
reconstruction methodologies, for computed tomography (CT) for interventional surgery. Discretizing
and implementing tomographic forward and backward operations is a crucial step in the design of model-
based iterative reconstruction algorithms in interventional CT, that we have investigated in the CIFRE
PhD thesis of Marion Savanier. The mathematical constraint of symmetry on the projector/backprojector
pair prevents linear interpolation, which is a standard method in analytical reconstruction, to be used.
Consequently, it often happens that these operators are approximated numerically, so that the adjoint
property is no longer fulfilled. In [8], we investigate fixed point algorithms stability properties when
such an adjoint mismatch arises. In [27], we focus on reconstructing regions of interest from a limited
number of CT measurements. We proposed to handle few-view truncated data thanks to a robust non-
convex data fidelity term combined with a sparsity inducing regularization function. We then apply the
deep unfolding paradigm to unroll a proximal algorithm, embedded in an iterative reweighted scheme,
allowing the learning of key parameters in a supervised manner, with a reduced inference time.

4.3.4 Reconstruction approaches in PET imaging

Participants: Emilie Chouzenoux, Alix Chazottes, Jean-Christophe Pesquet, Claire Rossignol (Collabora-
tion: F. Sureau, CEA)

Positron emission tomography (PET) is a quantitative functional imaging modality used to track the
fate and/or dynamics of a radiotracerpreviously injected into a patient. This technique is particularly used
in oncology for diagnosis and therapeutic monitoring, in the study ofneurodegenerative diseases, and in
pharmacology. In dynamic PET, the temporal evolution of the spatial distribution of the radiotracerduring
the examination is taken into account for the estimation of physiological parameters allowing for a fine
characterization of themolecular mechanisms at play (receptor concentration, absorption, dissociation
constants, binding potential, etc.). In the PhD thesis of Alix Chazottes, in collaboration between OPIS
and CEA, we aim to propose methodological developments in the fields ofoptimization and learning to
address the problem of robust dynamic PET reconstruction.

4.4 Graph mining applications

Geometric Graph Neural Networks for molecular and chemical systems

Participants: Alexandre Duval, Fragkiskos Malliaros (Collaboration: V. Schmidt, Alex Hernandez, Y.

Bengio, David Rolnick, Mila - Quebec AI Institute ; S. Miret, Intel Labs Berkeley)

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Inria Annual Report 2023

Graph Neural Networks (GNNs) currently constitute state-of-the-art models for solving prediction
tasks on graphs. Through the flexible formulation of the message passing mechanism, GNNs can learn
informative latent representations of graph entities at different resolution levels (e.g., node-, edge-, graph-
level). In many practical applications in molecular and chemical systems, the nodes of the graph have
associated geometric attributes (e.g., coordinates, velocities) related to their position in the 3D space.
In this context, geometric graphs represent the interaction of atoms in the 3D space, encapsulating a
range of physical symmetries such as rotations and translations. Existing GNN models often overlook
this aspect, rendering them ill-suited for prediction tasks on geometric graphs. Recently, Geometric
GNN architectures tailored to respect physical symmetries have emerged as flexible models of atomic
systems. Through an ongoing collaboration with Mila - Quebec AI Institute, Université de Montréal,
McGill University, and Intel Labs, we study geometric GNN models, focusing both on design principles as
well as on practical applications in materials modeling (e.g., property prediction and molecule generation)
[39].

4.4.1 Graph inference for gene regulation analysis

Participants: Fragkiskos Malliaros (Collaboration: A. Pirayre, F. Bidard, and L. Duval, IFPEN)

The discovery of novel gene regulatory processes improves the understanding of cell phenotypic
responses to external stimuli for many biological applications, such as medicine, environment or bio-
technologies. To this purpose, transcriptomic data are generated and analyzed from DNA microarrays or
more recently RNAseq experiments. They consist in genetic expression level sequences obtained for all
genes of a studied organism placed in different living conditions. From these data, gene regulation mech-
anisms can be recovered by revealing topological links encoded in graphs. In regulatory graphs, nodes
correspond to genes. A link between two nodes is identified if a regulation relationship exists between
the two corresponding genes. In our work, we propose to address this network inference problem with
recently developed techniques pertaining to graph optimization. Given all the pairwise gene regulation
information available, we propose to determine the presence of edges in the considered GRN by adopting
an energy optimization formulation integrating additional constraints. Either biological (information
about gene interactions) or structural (information about node connectivity) a priori are considered to
restrict the space of possible solutions. Different priors lead to different properties of the global cost
function, for which various optimization strategies, either discrete and continuous, can be applied.

4.4.2 Graph machine learning for spatiotemporal data

Participants: Fragkiskos Malliaros (Collaboration: J. Castro-Correa, Mohsen Badiey, Univ. of Delaware ; J.
H. Giraldo, Télécom Paris ;A. Mondal, Univ. of Surrey ; T. Bouwmans, La Rochelle Univ.)

Numerous real-world prediction problems involve spatiotemporal data. For example, consider
sensors scattered across diverse geographical regions measuring environmental conditions (e.g., temper-
ature, pollution) or functional magnetic resonance imaging (fMRI) data capturing brain activity. Both
scenarios generate data inherently rich in spatiotemporal structure, benefiting from the relational in-
ductive bias of graph-based modeling. In an ongoing collaboration with the University of Delaware,
Télécom Paris, and La Rochelle Université, we have introduced a methodology that leverages graph-based
modeling, enabling time series imputation with GNNs [36]. Major challenges here concern inducing
temporal and relational smoothness assumptions into the model as well as inferring the (often unknown)
graph structure. Furthermore, an intriguing aspect involves enhancing spatiotemporal graph models
with causal properties to capture causal influence effects among entities.

4.4.3 Graph representation learning for computational medicine

Participants: Emilie Chouzenoux, Fragkiskos Malliaros (Collaboration: A. Majumdar, IIIT Delhi ; G.
Chierchia, ESIEE Paris ; B. Liu, D. Papadopoulos, G. Tsoumakas, A. Papadopoulos, Aristotle Univ. of
Thessaloniki )

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Through the Associate International Inria Team COMPASS, led by Emilie Chouzenoux, we investigate
new models and inference tools to understand and predict optimal drug association, so as to tackle real-
life problems of computational drug discovery. We introduced graph-based regularization techniques in
order to incorporate expert knowledge and metadata in matrix completion tasks arising in the prediction
of drug-virus and drug-drug associations. Our recent works apply the proposed methodology to drug
repositioning [19, 21].

The discovery of drug-target interactions is also explored by Fragkiskos Malliaros, in collaboration
with Aristotle University of Thessaloniki. Accurately identifying reliable interactions among drugs and
proteins via computational methods, which typically leverage heterogeneous information retrieved from
diverse data sources, can boost the development of effective pharmaceuticals. We have considered multi-
layered network modeling to handle diverse drug and target similarities, introducing an optimization
framework called Multiple similarity DeepWalk-based Matrix Factorization (MDMF) for DTI prediction.
Current efforts in this direction involve leveraging Graph Neural Networks and self-supervised learning
tools.

4.4.4 Graph inference for time series analysis in earth and climate applications

Participants: Emilie Chouzenoux (Collaboration: V. Elvira, Univ. Edinburgh, UK ; J. Camps-Valls, Univ.
Valencia, Spain)

Studying the complex inter-dependencies in climate processes is a critical challenge. We use climate
models and analytical data to gain insight through observational causal discovery [31]. We revisit Granger
causality under a graphical perspective of state-space models. We investigate expectation-maximisation
algorithms [37] [68] for estimating matrix parameters in the state equation of a linear-Gaussian state-
space model under sparse priors, emphasizing both causal and correlation relationships among time
series samples. We evaluate our methods on climate-related problems, such as linking ENSO and the
North Atlantic Oscillation.

4.5 Other biomedical applications

4.5.1 Imaging biomarkers and characterization for chronic lung diseases

Participants: Othmane Laousy, Maria Vakalopoulou (Collaboration: S. Christodoulidis, G. Chassagnon,
M.-P. Revel, APHP ; N. Paragios, Therapanacea)

Diagnosis and staging of lung diseases is a major challenge for both patient care and approval of
new treatments. Among imaging techniques, computed tomography (CT) is the gold standard for in
vivo morphological assessment of lung anatomy currently offering the highest spatial resolution in lung
diseases. Although CT is widely used its optimal use in clinical practice and as an endpoint in clinical
trials remains controversial. Our goal in the PhD thesis of Othmane Laousy, is to provide automatic and
accurate tools that could help clinicians with their everyday practice.

4.5.2 AI for small bowel obstruction diagnosis

Participants: Alix Chazottes, Emilie Chouzenoux, Maxence Gélard, Jean-Christophe Pesquet (Collabora-
tion: Q. Vanderbecq, M. Zins, Hôpital Saint Joseph ; M. Wagner, LIB, Sorbonne Univ.)

Small bowel obstruction (SBO) is a common nontraumatic surgical emergency. All guidelines re-
commend computed tomography (CT) as the first-line imaging technique for patients with suspected
mechanical SBO with a four-fold goal: (i) to confirm or refute the diagnosis of SBO and, when SBO is
present, (ii) to locate the site of the obstruction, that is, the transition zone (iii) to identify the cause, and
(iv) to look for complications such as strangulation or perforation. Identifying SBO and differentiating its
causes (e.g., open-loop and closed-loop mechanisms) is time-consuming and subject to inter-observer
and intra-observer variability.

The aim of this collaborative project between Inria Saclay OPIS, Hôpital St Joseph, and LIB, Sorbonne

University, is to investigate AI approaches for a guided SBO diagnosis from 3D CT scans.

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4.5.3 A generative model for heart left ventricle perfusion analysis

Participant: Hugues Talbot (Collaboration: L. Najman, ESIEE Paris ; I. Vignon-Clementel, REO Team
leader, Inria ; C. Taylor, Heartflow Inc.)

Cardio-vascular diseases continue to be the leading cause of mortality in the world. Understanding
these diseases is a current, challenging and essential research project. The leading cause of heart
malfunction are stenoses causing ischemia in the coronary vessels. Current CT and MRI technology can
assess coronary diseases but are typically invasive, requiring risky catheterization and renal-toxic contrast
agents injection. In collaboration with the REO team headed by Irène Vignon-Clementel, and Heartflow,
a US based company, we have in the past contributed to Heartflow’s major product, that replaces these
physical exams with image-based exams only, limiting the use of contrast agents and in the cases that
do not require a stent insertion, eliminating catheterisation. Heartflow is current the market leader in
non-invasive coronary exams and the owner of most of the relevant IP in this domain.

Unfortunately, current imaging technology is unable to assess coronary disease along the full length
of coronary vessels. CT is limited to a resolution of about 1mm, whereas coronary vessels can be much
smaller, down to about 10 micrometers in diameter. To assess blood vessel health down to the smallest
sizes, blood perfusion imaging technique throughout the heart muscle must be used instead. Perfusion
imaging with PET or a Gamma camera, the current gold standard, is an invasive technology requiring the
use of radioactive tracers. To avoid using these, a lower quality estimate of perfusion can be achieved
using some ToF or injected gated MRI modalities.

We have investigated patient-specific vessel generation models together with porous model simula-
tions in order to propose a direct model of perfusion imaging, based on the known patient data, computer
flow dynamic simulations as well as experimental data consistent with known vessel and heart muscle
physiology. The objective of this work is to both to provide a useful, complex forward model of perfusion
image generation, and to solve the inverse problem of locating and assessing coronary diseases given a
perfusion exam, even though the affected vessels may be too small to be imaged directly.

Continuing on our work from the period 2015-2019, this year we proposed a functional myocar-
dial perfusion model consisting of the CT-derived segmented coronary vessels, a simulated vessel tree
consisting of several thousands of terminal vessels, filling the myocardium in a patient-specific way,
consistent with physiology data, physics-based and empirically-observed vessel growth rules, and a
porous medium. We produced and validated a CFD code capable of simulating blood flow in all three
coupled compartments, which allows us to simulate perfusion realistically.

5 Social and environmental responsibility

5.1 Footprint of research activities

The research carried out in OPIS aims at developing advanced techniques in the domain of data science
for precision medicine. One of the main features of this research is to ensure that the proposed methods
are not only efficient, but also grounded on sound mathematical foundations inherited from the areas
of optimization and fixed point algorithms. In the biomedical domain, it appears indeed mandatory
to guarantee the reliability and the explainability of the proposed approaches in their use by medical
doctors or producers of medical imaging devices.

5.2 Impact of research results

OPIS participates in the design of innovative products developed by big companies working in the domain
of medical imaging (GE Healthcare and Essilor) and several startups. Various application fields are
targeted (breast cancer detection, surgical radiology, interventional surgery, coronary disease monitoring,
vision correction, ...).

The methodological contributions of OPIS are far reaching, with impact going further the field of
medical imaging. OPIS transfers its expertise in artificial intelligence, image processing, and optimization
through collaboration with major industrial partners such as SNCF, Schneider Electrics, IFPEN, and
Thales. The transfer activity typically goes through CIFRE PhD contracts or more dedicated partnerships.

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In addition, OPIS has active collaborations with several hospitals, particularly Institut Gustave Roussy
and public hospitals from APHP in Paris. The purpose of these collaborations is to develop artificial
intelligence tools aiding medical doctors in their practice. A large part of this research activity is oriented
toward fighting against cancer using different kinds of data (CT scans, MRI, genomic data, histopathology
images,...). OPIS was also involved in several projects for helping to better diagnose and cure COVID-19
infection.

6 Highlights of the year

6.1 Awards

• The paper [48] has received the best poster award at the Information Processing In Medical Imaging

(IPMI) conference in 2023.

• The talk of Emilie Chouzenoux, on the paper [20], has received the best oral talk award at the
International Conference on Machine Learning and Intelligent Systems (MLIS) conference in 2023.

• The Master program DSBA is ranked 3rd worldwide in the QSWorld BusinessMaster Rankings in

2024.

7 New software, platforms, open data

7.1 New platforms

7.1.1 Prox Repository

Web site: Prox Repository

• Software Family: utility

• Audience: universe

• Evolution and maintenance: long term support

• Context/Role of OPIS: This website was created by E. Chouzenoux and J.-C. Pesquet from OPIS,
along with P.L. Combettes, North Carolina State University, and G. Chierchia, ESIEE Paris. The
maintenance is made by summer interns funded by OPIS, and by the authors of the website.

• Duration of the Development: The website was released in 2016, and is maintained regularly since

then.

• Proximity operators have become increasingly important tools as basic building blocks of proximal
splitting algorithms, a class of algorithms that decompose complex composite convex optimization
methods into simple steps involving one of the functions present in the model. This website
provides formulas for efficiently computing the proximity operator of various functions, along with
the associated codes in Matlab/Python langages.

• The codes provided are distributed under the licence CeCill-B.

7.1.2 The PINK image library

Web site: PINK

• Software Family: utility

• Audience: universe

• Evolution and maintenance: long term support

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• Context/Role of OPIS: H. Talbot is among the creators of this library and is still actively involved in

its maintenance.

• Duration of the Development: This software has been developed and maintained since 2011.

• The PINK image library is a general-purpose, open-source, portable image processing library
specializing in discrete geometry and mathematical morphology. It is the result of several decades of
research in these domains and features state-of-the art algorithmic implementation of both classical
and leading edge DG and MM operators. These include nD parallel thinning and skeletonization
methods and efficient hierarchical morphological transforms.

• This code is distributed under the CeCILL license.

7.1.3 The Vivabrain AngioTK toolkit

Web site: Vivabrain AngioTK toolkit

• Software Family: vehicle

• Audience: partners

• Evolution and maintenance: basic

• Context/Role of OPIS: H. Talbot participated to the programming of this software.

• Duration of the Development: This software has been released in 2018.

• AngioTK is a toolkit supported by Kitware (the authors of VTK) for the filtering, segmentation,
generation and simulation of blood vessels. It was started in the context of the Vivabrain ANR
project in 2012, but continues with the same as well as new partners. Applications are numerous,
from the simulation and understanding of perfusion (see associated theme) to the simulation of
realistic blood flow MRI images with associated ground truth, via the generation of blood vessel
atlases.

• This code is distributed under the Apache License, Version 2.0.

7.1.4 A scientific image viewer

Web site: imview

• Software Family: vehicle

• Audience: community

• Evolution and maintenance: basic

• Context/Role of OPIS: H. Talbot is the author of this software.

• Duration of the Development: This software has been released in 2001. His last version has been

updated in 2014.

• This general-purpose and cross-platform scientific image viewing tool has been part of the Debian
Linux distribution since 2001. This viewer is well adapted to display 2D with high-precision data
images (floating-point, etc), as well as 3D and hyper-spectral data. It features an interactive
segmentation tool for multispectral data and is scriptable.

• The codes provided are distributed under a GNU General Public License version 2.0 (GPLv2).

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7.1.5 TCGA segmentation

Web site: TCGA segmentation

• Software Family: vehicle

• Audience: community

• Evolution and maintenance: basic

• Context/Role of OPIS: This code was produced by the OPIS PhD student M. Le Rousseau.

• Duration of the Development: This software was developed during the PhD thesis of M. Le Rousseau,

mostly during the year 2020.

• This is a Python/Pytorch code that implements an end-to-end Whole Slide Imaging pre-processing
pipeline from The Cancer Genome Atlas download documents, as well as a complete implementa-
tion of deep learning tumor segmentation from WSI binary labels.

• The codes provided are distributed under a GNU Affero General Public License v3.0.

7.1.6 ScanCovIA

Web site: ScanCovIA

• Software Family: vehicle

• Audience: community

• Evolution and maintenance: basic

• Context/Role of OPIS: This software is the product of the ScanCovIA collaborative project between
OPIS, IGR, CentraleSupélec and the start-up Owkin. Several PhD students of OPIS were involved in
the programming of this software.

• Duration of the Development: This software was developed during the year 2020.

• This is a Python/Pytorch code allowing to reproduce the results of the ScanCovIA project.

• The codes provided are distributed under a GPL v3.0 license.

7.1.7 Graphical inference in linear-Gaussian state-space models

Web site: Graphical inference in linear-Gaussian state-space models

• Software Family: vehicle

• Audience: community

• Evolution and maintenance: basic

• Context/Role of OPIS: The code is produced by OPIS member Emilie Chouzenoux.

• Duration of the Development: This software has been developped from 2021. The most recent

release is from July 2023.

• This is a set of Matlab codes, to reproduce the experimental results from the articles [link] [68].

• The codes provided are distributed under CeCILL-B V1 license.

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7.1.8 Matrix factorization for drug discovery

Web site: Matrix factorization for drug discovery

• Software Family: vehicle

• Audience: community

• Evolution and maintenance: basic

• Context/Role of OPIS: The code is produced by several students from IIIT Delhi, in the context of

the Inria International Associate Team COMPASS.

• Duration of the Development: This software has been developped during the years 2022 and 2023.

• This is a set of Matlab codes, to reproduce the experimental results from the articles [link], [21],

[19].

• The codes provided are distributed through Github platform, under a General Public License v3.0.

7.1.9 Joint registration tumor segmentation

Web site: Joint registration tumor segmentation

• Software Family: vehicle

• Audience: community

• Evolution and maintenance: basic

• Context/Role of OPIS: This code was produced by the OPIS PhD student T. Estienne.

• Duration of the Development: This software was developed during the PhD thesis of T. Estienne,

mostly during the year 2019.

• This is a Python/Pytorch code that allows to reproduce the results of the paper [link].

• The codes provided are distributed under a General Public License v3.0.

7.1.10 PMCNet

Web site: PMCNet

• Software Family: vehicle

• Audience: community

• Evolution and maintenance: basic

• Context/Role of OPIS: This code was produced by the OPIS PhD student Yunshi Huang.

• Duration of the Development: This software was developed during the PhD thesis of Y. Yuang,

mostly during the year 2023.

• This is a Python/Pytorch code allowing to reproduce the results of the paper [20].

• The codes provided are distributed under a GNU General Public License v3.0.

Project OPIS

7.1.11 U-HQ

Web site: U-HQ

• Software Family: vehicle

• Audience: community

• Evolution and maintenance: basic

17

• Context/Role of OPIS: This code was produced by the OPIS PhD student Mouna Gharbi.

• Duration of the Development: This software was developed during the PhD thesis of M. Gharbi,

mostly during the year 2023.

• This is a Python/Pytorch code allowing to reproduce the results of the paper [16].

• The codes provided are distributed under a GNU General Public License v3.0.

7.1.12 FAENet

Web site: FAENet

• Software Family: vehicle

• Audience: community

• Evolution and maintenance: basic

• Context/Role of OPIS: This code has been produced by OPIS PhD student Alexandre Duval.

• Duration of the Development: This software is developed during the PhD thesis of A. Duval in 2023.

• This is a Python code allowing to reproduce the results of the paper [39].

• The codes provided are distributed under the licence GNU General Public License v3.0.

7.1.13 SJLR

Web site: SJLR

• Software Family: vehicle

• Audience: community

• Evolution and maintenance: basic

• Context/Role of OPIS: This code has been produced by J. Giraldo during his visit in OPIS in 2022.

• Duration of the Development: This software is developed in the context of a collaboration with J.

Giraldo (Télécom Paris).

• This is a Python code allowing to reproduce the results of the paper [41].

• The codes provided are distributed under the licence GNU General Public License v3.0.

Inria Annual Report 2023

18

7.1.14 GLIE

Web site: GLIE

• Software Family: vehicle

• Audience: community

• Evolution and maintenance: basic

• Context/Role of OPIS: This code has been produced by J. Giraldo during his visit in OPIS in 2022.

• Duration of the Development: This software is developed in the context of a collaboration with J.

Giraldo (Télécom Paris).

• This is a Python code allowing to reproduce the results of the paper [41].

• The codes provided are distributed under the licence GNU General Public License v3.0.

8 New results

8.1 Theoretical and practical advances for majorize-minimize algorithms

Participants: Emilie Chouzenoux, Jean-Baptiste Fest, Jean-Christophe Pesquet,Ségolène Martin.

In the context of large-scale, differentiable optimization, an important class of methods relies on the
principle of majorization-minimization (MM). MM algorithms are becoming increasingly popular in
signal/image processing and machine learning. MM approaches are fast, stable, require limited manual
settings, and are often preferred by practitioners in application domains such as medical imaging and
telecommunications. The work [9], we give conditions under which the sequence generated by the
resulting block majorize-minimize subspace algorithm converges to a critical point of the objective
function, in the non-convex setting. In [6], we investigate an asynchronous MM algorithm for solving
large scale differentiable non-convex optimization problems arising in microscopy. In the book chapter
[64], we highlight how MM approaches are fundamental tools, for solving optimization problems arising
in source separation for physics and chemistry applications.

8.2 Fundamental problems in image reconstruction

Participants: Emilie Chouzenoux, Jean-Christophe Pesquet, Fernando Marcelo Roldan Contreras (Col-
laboration: M. Savanier, CEA, A. Contreras, Universidad de Chile)

In inverse problems such as X-ray computed tomography, the applicability of the algorithm is dom-
inated by the cost of applying the forward linear operator and its adjoint at each iteration. In practice,
the adjoint operator is thus often replaced by an alternative operator with the aim to reduce the overall
computation burden and potentially improve conditioning issues. In [8, 70], we analyze the effect of
such an adjoint mismatch on the convergence of a large set of primal-dual proximal algorithms. We
derive conditions under which convergence of the algorithms to a fixed point is guaranteed. We also
derive bounds on the error between this point and the solution to the original minimization problem. We
illustrate our theoretical findings on image reconstruction tasks in computed tomography.

8.3 Deep unrolled algorithms for inverse problems in image and signal processing

Participants: Emilie Chouzenoux, Mouna Gharbi, Jean-Christophe Pesquet (Collaboration: C. Riddel, GE
Healthcare ; M. Savanier, CEA ; S. Villa, Univ. Genoa)

While model-based iterative methods can be used for solving inverse problems arising in image
processing, their practicability might be limited due to tedious parameterization and slow convergence.
In addition, inadequate solutions can be obtained when the retained priors do not perfectly fit the

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solution space. Deep learning methods offer an alternative approach that is fast, leverages information
from large data sets, and thus can reach high reconstruction quality. However, these methods usually rely
on black boxes not accounting for the physics of the imaging system, and their lack of interpretability
is often deplored. At the crossroads of both methods, unfolded deep learning techniques have been
recently proposed. They incorporate the physics of the model and iterative optimization algorithms into
a neural network design, leading to superior performance in various applications.

In [27], we address the problem of image reconstruction for region-of-interest (ROI) computed
tomography (CT). We introduced a novel, unfolded deep learning approach called U-RDBFB designed
for ROI CT reconstruction from limited data. Few-view truncated data are efficiently handled thanks to
a robust non-convex data fidelity function combined with sparsity inducing regularization functions.
Iterations of a block dual forward-backward algorithm, embedded in an iterative reweighted scheme,
are then unrolled over a neural network architecture, allowing the learning of various parameters in
a supervised manner. Our experiments show an improvement over various state-of-the-art methods,
including model-based iterative schemes, deep learning architectures, and deep unfolding methods.

In [16], we propose a deep neural network based on unrolling a Half-Quadratic algorithm to address
the problem of sparse signal reconstruction arising in analytical chemistry. This allows us to build
interpretable layers mirroring iterations, making it possible to learn automatically data-driven hyperpara-
meters such as regularization and stepsizes. Furthermore, we propose a dictionary of custom activation
functions derived from potentials used in the original variational model. This interpretation of activations
can be useful for analyzing the stability of neural networks. The efficiency of our method in comparison to
iterative and learning-based methods is showcased through various experiments conducted on realistic
mass spectrometry databases with various blur kernels and noise levels. Deep unrolling of primal-dual
proximal algorithms has also been considered in [72], for the same application context.

8.4 Computational approaches for multiphotonic image restoration

Participants: Emilie Chouzenoux, Jean-Baptiste Fest, Ségolène Martin, Jean-Christophe Pesquet (Collab-
oration: C. Lefort, XLIM, CNRS ; M. Chalvidal, ANITI Toulouse ; J. Ajdenbaum, Univ. Paris Saclay)

We investigate image restoration approaches in the context of the development of novel laser strategies
in multiphoton microscopy (MPM). The resolution of the MPM device is quantified by a procedure of
point-spread-function (PSF) assessment led by an original, robust, and reliable computational approach.
The estimated values for the PSF width are shown to be comparable to standard values found in optical
microscopy. Advanced optimization methods taking advantage of modern multicores computing devices
have been developed [6]. This allows to deduce a new instrumental and computational pipeline for
MPM of biomedical structures. We demonstrate in [13] the interest of our pipeline for imaging bacteria
without any labelling process. In [65], we present a novel approach for addressing the MPM image
restoration inverse problem in an end-to-end fashion. Our comprehensive restoration pipeline revisits
the conventional restoration protocol from acquisition to the final restored outcome.

8.5 Monte-Carlo approaches for global optimization

Participants: Emilie Chouzenoux, Thomas Guilmeau (Collaboration: V. Elvira, University of Edinburgh)

Finding the global minimum of a nonconvex optimization problem is a notoriously hard task appear-
ing in numerous applications, from signal processing to machine learning. In [44], we introduce a new
simulated annealing approach that selects the cooling schedule on the fly. Starting from a variational for-
mulation of the problem of joint temperature and proposal adaptation, we derive an alternating Bregman
proximal algorithm to minimize the resulting cost, obtaining the sequence of Boltzmann distributions
and proposals.

8.6 Fast schemes for adaptive importance sampling

Participants: Emilie Chouzenoux, Yunshi Huang, Jean-Christophe Pesquet (Collaboration: V. Elvira,
University of Edinburgh, UK ; O. D. Akyildiz, Imperial College London, UK ; L. Martino, University Rey

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Inria Annual Report 2023

Juan Carlos, Madrid)

Adaptive importance sampling (AIS) methods are increasingly used for the approximation of distribu-
tions and related intractable integrals in the context of Bayesian inference. In [20], we propose a novel
algorithm PMCnet that includes an efficient AIS mechanism, to efficiently explore the highly multimodal
posterior distribution involved in the training of Bayesian Neural Networks. Numerical results illustrate
the excellent performance and the improved exploration capabilities of PMCnet for the training of both
shallow and deep neural networks. In [15], we propose an AIS method, called GRAMIS, that iteratively
improves the set of proposals by exploiting geometric information of the target to adapt the location and
scale parameters of those proposals. A repulsion term is introduced that favors a coordinated exploration
of the state space. We provide a theoretical justification of the repulsion term and show the good perform-
ance of GRAMIS in problems where the target cannot be easily approximated by a standard uni-modal
proposal.

8.7 Computational approaches for drug discovery

Participant: Emilie Chouzenoux (Collaborations: S. Chatterjee, S. Jain, P. Gupta, and A. Majumdar, IIIT
Delhi)

The discovery of drug-target interactions (DTIs) is a very promising area of research with great
potential. The accurate identification of reliable interactions among drugs and proteins via computational
methods, which typically leverage heterogeneous information retrieved from diverse data sources, can
boost the development of effective pharmaceuticals.

In [21], we extend the previous approach by incorporating expert knowledge metadata and deep
factorization term in our formulation. Our algorithm is applied to in silico predictions of anti-virals for
monkeypox. In [19], we propose a Siamese-like architecture with two processing channels’ networks
based on deep convolutional transform learning for drug-drug interaction prediction.

We recently focused on computational models for repurposing drugs with the potential for treating
drug resistant bacterial infections. In [66], we produced a dataset for drug-bacteria associations (DBA)
that affects humans, and conduct genomic similarity computations for all known bacteria impacting
humans and assess structural similarities for all antibiotic drugs.

8.8 Expectation-Maximization for time series modeling and inference

Participants: Emilie Chouzenoux (Collaboration: S. Sharma, A. Majumdar, IIIT Delhi, India ; V. Elvira,
University of Edinburgh ; J. Cerda and G. Camps-Valls, Universitat de València, Spain)

Modeling and inference with multivariate sequences is central in a number of signal processing
applications such as acoustics, social network analysis, biomedical, and finance, to name a few. The
linear-Gaussian state-space model is a common way to describe a time series through the evolution of a
hidden state, with the advantage of presenting a simple inference procedure due to the celebrated Kalman
filter. A fundamental question when analyzing multivariate sequences is the search for relationships
between their entries (or the modeled hidden states), especially when the inherent structure is a non-fully
connected graph. In such context, graphical modeling combined with parsimony constraints allows
to limit the proliferation of parameters and enables a compact data representation which is easier to
interpret by the experts.

We recently introduced a novel perspective by relating this matrix to the adjacency matrix of a directed
graph, also interpreted as the causal relationship among state dimensions in the Granger-causality sense.
Under this perspective, we propose in [37], a majorization-minimization algorithm for estimating the
sought graph under a non-convex sparsity prior. In [31], we illustrate the benefits of the proposed
graph model and inference technique over standard Granger causality methods, on challenging climate
problems. In [79], we specialize the proposed methodology to the problem of stock forecasting, by
proposing an online training strategy and a probabilistic assessment of the trading decision. A multi-layer
model is considered in [28], to address the prediction of day-ahead crypto-currency prices.

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8.9 Advanced nonconvex priors for signal and image restoration

Participants: Emilie Chouzenoux, Jean-Christophe Pesquet, Gabriele Scrivanti(Collaboration: L. Duval,
IFPEN ; P. Zheng, Aachen University ; M.C. Corbineau, Preligens)

The core of many approaches for the resolution of variational inverse problems arising in signal
and image processing consists of promoting the sought solution to have a sparse representation in a
well-suited space. A crucial task in this context is the choice of a good sparsity prior that can ensure a
good trade-off between the quality of the solution and the resulting computational cost.

In [67], we propose a novel nonsmooth and nonconvex variational formulation of the problem of
joint problem of reconstruction/feature extraction. For this purpose, we introduce a versatile generalised
Gaussian prior whose parameters, including its exponents, are space-variant. Secondly, we design an
alternating proximal-based optimisation algorithm that efficiently exploits the structure of the proposed
nonconvex objective function. We also analyze the convergence of this algorithm. As shown in numerical
experiments conducted on joint segmentation/deblurring tasks, the proposed method provides high-
quality results.

In [30], we focus on the inverse problem of joint trend removal and blind deconvolution of sparse
peak-like signals. It blends a parsimonious prior with the hypothesis that smooth trend and noise can
somewhat be separated by low-pass filtering. We combine the generalized quasi-norm ratio SOOT/SPOQ
sparse penalties with the BEADS ternary assisted source separation algorithm. This results in a both
convergent and efficient tool, with a novel Trust-Region block alternating variable metric forward-
backward approach.

8.10 Interpretable ensembling rules

Participants: Emilie Chouzenoux, Jean-Christophe Pesquet, Nguyen Vu (Collaboration: I. Ben-Ayed, ETS
Montréal, Canada)

Ensemble learning leverages multiple models (i.e., weak learners) on a common machine learning
task to enhance prediction performance. Basic ensembling approaches average the weak learners outputs,
while more sophisticated ones stack a machine learning model in between the weak learners outputs and
the final prediction. The work [81] fuses both aforementioned frameworks. We introduce an aggregated f-
average (AFA) shallow neural network which models and combines different types of averages to perform
an optimal aggregation of the weak learners predictions. We emphasise its interpretable architecture and
simple training strategy, and illustrate its good performance on the problem of few-shot class incremental
learning.

8.11 Novel joint learning approaches for neural networks based multiscale trans-

forms

Participants: Tassnim Dardouri, Mounir Kaaniche, Jean-Christophe Pesquet(Collaboration: A. Benazza-
Benyahia, SUPCOM Tunis, G. Dauphin, Univ. Sorbonne Paris Nord)

Let us recall that a neural network based lifting decomposition, where the prediction and updates
steps are performed using an FCNN, has been proposed in 2021. While the different FCNN models have
been separately learned in this work, we have recently proposed novel joint learning approaches to find
the optimal FCNN models in [11]. The latter aim to learn both the FCNN prediction and update models
simultaneously. To this end, a multi-level optimization technique has been proposed. This technique
consists in interpreting the lifting-based multiresolution decomposition as a full architecture whose
involved FCNN models are globally learned at the same time through a unique loss function. In this
respect, two new loss functions are investigated. While the first one resorts to a weighted sum of the
loss functions used to optimize the prediction and update stages, the second one aims to obtain a good
approximation to rate-distortion functions.

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Inria Annual Report 2023

8.12 Designing robust neural networks

Participants: Ana Neacsu, Jean-Christophe Pesquet(collaboration: C. Burileanu, University Polithenica
of Bucarest)

We introduce in [25], ABBA networks, a novel class of (almost) non-negative neural networks, which
are shown to possess a series of appealing properties. In particular, we demonstrate that these networks
are universal approximators while enjoying the advantages of non-negative weighted networks. We derive
tight Lipschitz bounds both in the fully connected and convolutional cases. We propose a strategy for
designing ABBA nets that are robust against adversarial attacks, by finely controlling the Lipschitz constant
of the network during the training phase. We show that our method outperforms other state-of-the-art
defenses against adversarial white-box attackers. Experiments are performed on image classification
tasks on four benchmark datasets.

We introduce in [26] a novel approach for building a robust Automatic Gesture Recognition system
based on Surface Electromyographic (sEMG) signals, acquired at the forearm level. Our main contribution
is to propose new constrained learning strategies that ensure robustness against adversarial perturbations
by controlling the Lipschitz constant of the classifier. We focus on positive neural networks for which
accurate Lipschitz bounds can be derived, and we propose different spectral norm constraints offering
robustness guarantees from a theoretical viewpoint. Experimental results on two distinct datasets
highlight that a good trade-off in terms of accuracy and performance is achieved. We then demonstrate
the robustness of our models, compared to standard trained classifiers in three scenarios, considering
both white-box and black-box attacks.

8.13 Neural speed-torque estimator for induction motors

Participants: Jean-Christophe Pesquet, Sagar Verma (collaboration: N. Henwood, A. K. Jebai, Schneider
Electric ; Marc Castella, Télécom SudParis)

A neural network (NN) approach is introduced in [29] to estimate the nonnoisy speed and torque
from noisy measured currents and voltages in induction motors with variable speed drives. The proposed
estimation method is comprised of a neural speed-torque estimator and a neural signal denoiser. A new
training strategy is introduced that combines large amount of simulated data and a small amount of
real-world data. The proposed denoiser does not require nonnoisy ground-truth data for training, and
instead uses classification labels that are easily generated from real-world data. This approach improves
upon existing noise removal techniques by learning to denoise as well as classify noisy signals into static
and dynamic parts. The proposed NN-based denoiser generates clean estimates of currents and voltages
that are then used as inputs to the NN estimator of speed and torque. Extensive experiments show
that the proposed joint denoising-estimation strategy performs very well on real data benchmarks. The
proposed denoising method is shown to outperform several widely used denoising methods and a proper
ablation study of the proposed method is conducted.

8.14 Proximal splitting adversarial attack for semantic segmentation

Participant: Jean-Christophe Pesquet(Collaborations: I. Ben Ayed, J. Rony, ETCS Montréal)

Classification has been the focal point of research on adversarial attacks, but only a few works invest-
igate methods suited to denser prediction tasks, such as semantic segmentation. The methods proposed
in these works do not accurately solve the adversarial segmentation problem and, therefore, overestimate
the size of the perturbations required to fool models. In [53], we propose a white-box attack for these
models based on a proximal splitting to produce adversarial perturbations with much smaller ℓ∞ norms.
Our attack can handle large numbers of constraints within a nonconvex minimization framework via an
Augmented Lagrangian approach, coupled with adaptive constraint scaling and masking strategies. We
demonstrate that our attack significantly outperforms previously proposed ones, as well as classification
attacks that we adapted for segmentation, providing a first comprehensive benchmark for this dense task.

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8.15 Artificial intelligence for radiology and medicine, with application to immuno-

therapy

Participants: Theodore Aouad, Younes Belkouchi, Hugues Talbot (Collaboration: S. Ammari, N. Lassau,
Institut Gustave Roussy ; A. Feydy, Hôpital Cochin ; N. Gogin, GE Healthcare)

Radiology in medicine is a major domaine of research and applications in Artificial Intelligence. A
critical aspect in radiology for cancer is the development of new and effective criteria for the assess-
ment of patients based on imaging data in particular. One widely use such criteria in the context of
immunotherapy are RECIST and iRECIST, to evaluate certain types of atypical responses observed under
immunotherapy, namely pseudoprogression. They are still being evaluated, and aim at a consistent
record of responses to therapies. One drawback is of this criterion is an inescapable delay of one month
between a baseline and second MRI or scanner to assess lesion progression.

We also exhibited a correlation between total metastatic volume as measured on CT scans and the
presence of ctDNA (circulant tumoral DNA) in liquid biopsies on a prospective study of more than 1000
patients. This is important because liquid biopsies have the potential to be cheaper, faster and easier to
interpret than CT scans, particularly in countries where CT scanner are scarse [60].

Some of our early investigative efforts on the prediction of patient outcome regarding the treatment
of cancer patients with immunotherapy was published in [61]. In [12] we proposed to estimate the 3D
fat and muscle masses as prognostic markers. We continued our work on the diagnosis and prognosis
of Spondylo-Arthritis, which is a debilitiating auto-immune disease difficult to diagnose on MRI data
of the pelvis [5]. The use of AI provides an additional opinion and also an ability to diagnose the illness
by non-specialist clinicians, alleviating the need for rare expertise in this type of imaging. This year we
have experienced difficulties in getting access to the AP-HP datacenter but managed to continue our
investigation efforts. We also recently validated a deep-learning methods used for musculo-skeletal
imaging to help diagnose a relatively rare disease: axial spondylo-arthritis, an auto-immune disease, on a
cohort provided by UCB Pharma [76] with good results.

8.16 Interpretable deep learning via binary morphological neural networks

Participants: Theodore Aouad, Hugues Talbot

Deep learning has enabled a lot of progress in computer vision tasks in the last 10 years. However, a
widely acknowledge element is that deep network results are not always stable or easy to interpret. Also
deep network require significant computing resources in both computation and memory, often requiring
graphical processing units (GPUs) for both training and inference. A recent popular topic of interest is to
study whether neural networks can be drastically simplified using only binary weights. By construction
this would also regularize networks. There can also be a benefit in interpretability because complex
networks would essentially be learning compositions of mathematical morphology operators, which are
often subject to expected mathematical behaviour.

However, binary networks are difficult to train. In this work we defined a notion of binary morpholo-
gical neuron and we build neural networks to use these as their building blocks instead of convolutions.
This makes scientific sense since using morphological operators can be thought as using activated linear
filters, which is a basic construction mechanism in most CNNs. In this manner, training can be performed
naturally using existing frameworks (e.g. pytorch), then when training is completed, the weights can
be binarized with a simple method without loss of performance. Interestingly, recent work has shown
that binarized networks can be trained using an effective subgradient descent method using a proximal
operator interpretation. This is a very promising avenue of research, and we are currently working on
extending this work to more complex networks and to other types of morphological operators.

We have shown in [34] that these networks can indeed learn sequences of operators as well as perform

classification tasks with good results.

8.17 Deep generative method for musculo-skeletal imaging

Participants: Hugues Talbot (Collaboration: Y. Gu, Y. Otake, Y. Sato, Nara Institute of Science and Techno-
logy, Japan)

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Inria Annual Report 2023

We have started a strong collaboration with NAIST on the use of generative methods for the extim-
ation of osteo-porosis from plain X-Ray images. This is a major public health issue, and the use of AI
methods can help alleviate the need for expensive and invasive exams such as DEXA (dual energy X-ray
absorptiometry) [43].

8.18 Whole-slide pathology analysis

Participants: Loïc Le Bescond, Maria Vakalopoulou, Stergios Christodoulidis and Hugues Talbot with
Marvin Lerousseau, Ingrid Garberis and Fabrice André, Gustave-Roussy.

Whole-slide image analysis is a challenging area of research in medical imaging. The goal is to analyze
a whole slide of tissue, which can be several gigapixels in size, and to extract relevant information from
it. This is a challenging task because of the size of the images, but also because of the complexity of the
tissue, which can contain many different types of cells, and because of the presence of artefacts such as
folds, tears, or stains.

This year we published a major analysis conducted at Gustave-Roussy concerning the response of
some breast cancers with variable genetic expression to a novel immunotherapy regimen. This was
published in [23] and potential to be highly influential in the field of breast cancer treatment.

We also published a novel study on the importance of Out-of-Distribution samples in the context of
Multiple-Instance Learning, a methodology that can help learning methods to cope with the very large
size of the data, by considering only samples in a bag, rather than individual samples. This work was
published in [75] and is being prepared for journal publication.

8.19 Certification of Segmentation Networks using Diffusion Models

Participants: Othmane Laousy, Maria Vakalopoulou(Collaborations: MICS Centrale-Supelec, New York
University, Paris Cite University, Therapanacea)

The robustness of image segmentation has been an important research topic in the past few years
as segmentation models have reached production-level accuracy. However, like classification models,
segmentation models can be vulnerable to adversarial perturbations, which hinders their use in critical-
decision systems like healthcare or autonomous driving. Recently, randomized smoothing has been
proposed to certify segmentation predictions by adding Gaussian noise to the input to obtain theoretical
guarantees. However, this method exhibits a trade-off between the amount of added noise and the level of
certification achieved. In this paper, we address the problem of certifying segmentation prediction using
a combination of randomized smoothing and diffusion models. We performed extensive experiments to
prove the effectiveness of the method in computer vision [32], and medical imaging [47].

8.20 Computational Pathology and Foundation Models

Participant: Maria Vakalopoulou (Collaborations: MICS CentraleSupelec, Stony Brook University)

Whole slide image (WSI) classification is a critical task in computational pathology, requiring the
processing of gigapixel-sized images, which is challenging for current deep-learning methods. The
current state of the art methods are based on multi-instance learning schemes (MIL), which usually rely
on pretrained features to represent the instances. Due to the lack of task-specific annotated data, these
features are either obtained from well-established backbones on natural images, or, more recently from
self supervised models pretrained on histopathology. However, both approaches yield task-agnostic
features, resulting in performance loss compared to the appropriate task-related supervision, if available.
In [57], we show that when task specific annotations are limited, we can inject such supervision into
downstream task training, to reduce the gap between fully task-tuned and task agnostic features. We
propose Prompt-MIL, an MIL framework that integrates prompts into WSI classification. Prompt-MIL
adopts a prompt tuning mechanism, where only a small fraction of parameters calibrates the pretrained
features to encode task-specific information, rather than the conventional full fine-tuning approaches.
Moreover, in [58], we adapt SAM for semantic segmentation by first introducing trainable class prompts,
followed by further enhancements through the incorporation of a pathology encoder, specifically a

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pathology foundation model. Our framework, SAM-Path enhances SAM’s ability to conduct semantic
segmentation in digital pathology without human input prompts.

8.21 Large Language Models for prognosis of the renal transplant

Participants: Léo Milecki, Maria Vakalopoulou (Collaborations: CHU Necker, Ecole Polytechnique)

Renal transplantation emerges as the most effective solution for end-stage renal disease. Occurring
from complex causes, a substantial risk of transplant chronic dysfunction persists and may lead to
graft loss. Medical imaging plays a substantial role in renal transplant monitoring in clinical practice.
However, graft supervision is multi-disciplinary, notably joining nephrology, urology, and radiology,
while identifying robust biomarkers from such high-dimensional and complex data for prognosis is
challenging. In this work, taking inspiration from the recent success of Large Language Models (LLMs),
we propose MEDIMP – Medical Images with clinical Prompts – a model to learn meaningful multi-
modal representations of renal transplant Dynamic Contrast-Enhanced Magnetic Resonance Imaging
by incorporating structural clinico biological data after translating them into text prompts. MEDIMP
[milecki:hal-04040697] is based on contrastive learning from joint text-image paired embeddings to
perform this challenging task. Moreover, we propose a framework that generates medical prompts using
automatic textual data augmentations from LLMs. Our goal is to learn meaningful manifolds of renal
transplant DCE MRI, interesting for the prognosis of the transplant or patient status (2, 3, and 4 years
after the transplant), fully exploiting the limited available multi-modal data most efficiently. Extensive
experiments and comparisons with other renal transplant representation learning methods with limited
data prove the effectiveness of MEDIMP in a relevant clinical setting, giving new directions toward
medical prompts.

8.22 Differentiating Nonsmooth Solutions to Parametric Monotone Inclusion Prob-

lems

Participant: Tony Silveti-Falls (Collaboration: J. Bolte, TSE, Toulouse ; E. Pauwels, IRIT, Toulouse)

We leverage in [4], path differentiability and a recent result on nonsmooth implicit differentiation
calculus to give sufficient conditions ensuring that the solution to a monotone inclusion problem will
be path differentiable, with formulas for computing its generalized gradient. A direct consequence of
our result is that these solutions happen to be differentiable almost everywhere. Our approach is fully
compatible with automatic differentiation and comes with assumptions which are easy to check, roughly
speaking: semialgebraicity and strong monotonicity. We illustrate the scope of our results by considering
three fundamental composite problem settings: strongly convex problems, dual solutions to convex
minimization problems and primal-dual solutions to min-max problems.

8.23 Geometric GNNs for materials modeling

Participants: Alexandre Duval, Fragkiskos Malliaros (Collaborations: V. Schmidt, A. Hernandez, . Bengio,
D. Rolnick, Mila - Quebec AI Institute ; S. Miret, Intel Labs Berkeley)

Applications of machine learning techniques for materials modeling typically involve functions known
to be equivariant or invariant to specific symmetries. While graph neural networks (GNNs) have proven
successful in such tasks, they enforce symmetries via the model architecture, which often reduces their
expressivity, scalability and comprehensibility. In our recent work [39], we have introduced (1) a flexible
framework relying on stochastic frame-averaging (SFA) to make any model E(3)-equivariant or invariant
through data transformations. (2) FAENet: a simple, fast and expressive GNN, optimized for SFA, that
processes geometric information without any symmetrypreserving design constraints. We have shown
the validity of our method theoretically, and have empirically demonstrated its superior accuracy and
computational scalability in materials modeling on the OC20 dataset (S2EF, IS2RE) as well as common
molecular modeling tasks (QM9, QM7-X).

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Inria Annual Report 2023

8.24 Spatio-temporal data analysis with GNNs

Participants: Fragkiskos Malliaros (Collaboration: J. Castro-Correa, M. Badiey, Univ. of Delaware ; J. H.
Giraldo, Télécom Paris ; Andindya Mondal, Univ. of Surrey ; T. Bouwmans, La Rochelle Univ.)

The recovery of time-varying graph signals is a fundamental problem with numerous applications in
sensor networks and forecasting in time series. Effectively capturing the spatio-temporal information
in these signals is essential for the downstream tasks. Previous studies have used the smoothness of
the temporal differences of such graph signals as an initial assumption. Nevertheless, this smoothness
assumption could result in a degradation of performance in the corresponding application when the
prior does not hold. In this work, we relax the requirement of this hypothesis by including a learning
module. In our recent work, we have proposed a Time Graph Neural Network (TimeGNN) for the recovery
of time-varying graph signals [36]. Our algorithm uses an encoder-decoder architecture with a specialized
loss composed of a mean squared error function and a Sobolev smoothness operator.TimeGNN has
shown competitive performance against previous methods in real datasets.

8.25 Towards deep GNN architecture

Participant: Fragkiskos Malliaros (Collaboration: J. H. Giraldo, Télécom Paris ; K. Skianis, BLUEAI ; T.
Bouwmans, La Rochelle Univ.)

GNNs have succeeded in various computer science applications, yet deep GNNs underperform
their shallow counterparts despite deep learning’s success in other domains. Over-smoothing and over-
squashing are key challenges when stacking graph convolutional layers, hindering deep representation
learning and information propagation from distant nodes. Our recent work reveals that over-smoothing
and over-squashing are intrinsically related to the spectral gap of the graph Laplacian, resulting in an
inevitable trade-off between these two issues, as they cannot be alleviated simultaneously [41]. To
achieve a suitable compromise, we have proposed adding and removing edges as a viable approach. We
introduce the Stochastic Jost and Liu Curvature Rewiring (SJLR) algorithm, which is computationally
efficient and preserves fundamental properties compared to previous curvature-based methods. Unlike
existing approaches, SJLR performs edge addition and removal during GNN training while maintaining
the graph unchanged during testing. Comprehensive comparisons have demonstrated SJLR’s competitive
performance in addressing over-smoothing and over-squashing.

8.26 Higher-order sparse convolutions in GNNs

Participant: Fragkiskos Malliaros (Collaboration: J. H. Giraldo, Télécom Paris ; Sajid Javed, Khalifa Univ. ;
A. Mahmood, Information Technology University ; T. Bouwmans, La Rochelle Univ.)

Capturing higher-order relationships between nodes is crucial to increase the expressive power of
GNNs. However, existing methods to capture these relationships could be infeasible for large-scale graphs.
In our work, we have introduced a new higher-order sparse convolution based on the Sobolev norm of
graph signals [42]. Our Sparse Sobolev GNN (S-SobGNN) computes a cascade of filters on each layer
with increasing Hadamard powers to get a more diverse set of functions, and then a linear combination
layer weights the embeddings of each filter. We have evaluated S-SobGNN in several applications of semi-
supervised learning, showing competitive performance compared to several state-of-the-art methods.

8.27 Influence maximization with GNNs

Participant: Fragkiskos Malliaros (Collaboration: G. Panagopoulos, M. Vazirgiannis, École Polytechnique
; N. Tziortziotis, Jellyfish)

Influence maximization is a well-studied combinatorial optimization problem. Finding the seed
set that maximizes the influence spread over a network is known to be an NP-hard problem. Though a
greedy algorithm can provide near-optimal solutions, the subproblem of influence estimation renders
the solutions inefficient. In our recent work, we have proposed GLIE, a graph neural network that learns
how to estimate the influence spread of the independent cascade [51]. GLIE relies on a theoretical upper
bound that is tightened through supervised training. Experiments indicate that it provides accurate
influence estimation for real graphs up to 10 times larger than the train set. Subsequently, we have

Project OPIS

27

incorporated it into two influence maximization techniques. The proposed algorithms are inductive,
meaning they are trained on graphs with less than 300 nodes and up to 5 seeds, and tested on graphs
with millions of nodes and up to 200 seeds. The final method exhibits the most promising combination
of time efficiency and influence quality, outperforming several baselines.

9 Bilateral contracts and grants with industry

9.1 Bilateral contracts with industry

• PhD Contract with Heartflow Inc

– Project title: Estimating heart perfusion based on physics-aware machine-learning methods

– Duration: 2022-2025

– Leaders: Hugues Talbot and I. Vignon-Clementel (Inria REO).

• PhD Contract with SAFRAN (CIFRE)

– Project title: Safran fibres composites

– Duration: 2023-2026

– Leaders: Hugues Talbot

• Contract with Schneider Electric

– Project title: AI for electrical motor diagnosis and control

– Duration: 2022-2024

– Leaders: M. Castella (Télécom ParisTech) and Jean-Christophe Pesquet

• Contract with ESSILOR

– Project title: Software development for photo-refraction analysis

– Duration: 2020-2024

– Leaders: Emilie Chouzenoux and Jean-Christophe Pesquet

• Contract with AIRBUS (Plan de Relance - mesure de préservation de l’emploi en R&D)

– Project title: AI for composite quality

– Duration: 2022-2024

– Leaders: Nora Ouzir and Jean-Christophe Pesquet

10 Partnerships and cooperations

10.1 International initiatives

10.1.1 Inria associate team not involved in an IIL or an international program

COMPASS

Title: Computational medicine for optimal drug association

Duration: 2021 -> 2023

Coordinator: Angshul Majumdar (angshul@iiitd.ac.in)

Partners:

• Indraprastha Institute of Information Technology New Delhi (Inde)

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Inria Annual Report 2023

Inria coordinator: Emilie Chouzenoux

Summary: Through this Associate Team proposal, we intend to foster a collaboration between the Inria
project-team OPIS, and the Salsa laboratory in IIIT Delhi, India. The partners will investigate new
models and inference tools to understand and predict optimal drug association, so as to tackle
real-life problems of biopharmaceutical science. Expected contributions lie in the production of
two curated open databases, and in methodological developments within the fields of data mining,
graphical modeling, representation learning and optimization/game theory.

Website: COMPASS

10.2 International research visitors

10.2.1 Visits of international scientists

Amel Benazza Benyahia

Status Professor

Institution of origin: SUPCOM Tunis

Country: Tunisia

Dates: June 2023 to July 2023

Context of the visit: Research collaboration with E. Chouzenoux, and J.-C. Pesquet.

Mobility program/type of mobility: Research stay funded by CentraleSupélec.

Hamid Krim

Status Professor

Institution of origin: North Carolina University

Country: USA

Dates: May 2023 to June 2023

Context of the visit: Research collaboration with E. Chouzenoux, and J.-C. Pesquet.

Mobility program/type of mobility: Research stay funded by ERC MAJORIS.

Angshul Majumdar

Status Associate Professor

Institution of origin: IIIT Delhi

Country: India

Dates: May 2023 to June 2023

Context of the visit: Research collaboration with E. Chouzenoux, and J.-C. Pesquet.

Mobility program/type of mobility: Research stay funded by COMPASS project.

Project OPIS

Anchalee Sripattanet

Status PhD student

29

Institution of origin: King Montut’s Institut of Technology, Bangkok

Country: Thailand

Dates: Nov. 2022 to Apr. 2023

Context of the visit: Research collaboration with J.-C. Pesquet.

Mobility program/type of mobility: Research stay funded by National Research Council of Thailand

and CVN.

Kristian Vega

Status PhD student

Institution of origin: University of Genoa

Country: Italy

Dates: May 2023 to Aug. 2023

Context of the visit: Secondment visit in the context of ITN TradeOpt.

Mobility program/type of mobility: Research stay funded by ITN TradeOpt.

10.2.2 Visits to international teams

Research stays abroad

Nguyen Vu

Visited institution: ÉTS Montréal

Country: Canada

Dates: 14th January - 22nd April 2023

Context of the visit: Collaboration with Ismail Ben Ayed

Mobility program/type of mobility: Visiting research internship, funded by DATAIA

Thomas Guilmeau

Visited institution: University of Edinburgh

Country: United Kingdom

Dates: 21 Feb. to 05 May 2023

Context of the visit: Collaboration with Víctor Elvira

Mobility program/type of mobility: Visiting research internship, funded by ERC MAJORIS

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Inria Annual Report 2023

10.3 European initiatives

10.3.1 H2020 projects

MAJORIS MAJORIS project on cordis.europa.eu

Title: Majoration-Minimization algorithms for Image Processing

Duration: From January 1, 2020 to June 30, 2025

Partners:

• Inria Saclay, OPIS, France

Coordinator: Emilie Chouzenoux

Summary: Mathematical optimization is the key to solving many problems in science, based on the
observation that physical systems obey a general principle of least action. While some problems
can be solved analytically, many more can only be solved via numerical algorithms. Research in
this domain has proved essential over many years. In addition, science in general is changing. In-
creasingly, in biology, medicine, astronomy, chemistry, physics, large amounts of data are collected
by constantly improving signal and image acquisition devices, that must be analyzed by sophistic-
ated optimization tools. In this proposal, we consider handling optimization problems with large
datasets. This means minimizing a cost function with a complex structure and many variables. The
computational load for solving these problems is too great for even state-of-the-art algorithms.
Thus, only relatively rudimentary data processing techniques are employed, reducing the quality
of the results and limiting the outcomes that can be achieved via these novel instruments. New
algorithms must be designed with computational scalability, robustness and versatility in mind.

In this context, Majorization-Minimization (MM) approaches have a crucial role to play. They con-
sist of a class of efficient and effective optimization algorithms that benefit from solid theoretical
foundations. The MAJORIS project aims at proposing a breakthrough in MM algorithms, so that
they remain efficient when dealing with big data. I propose to tackle several challenging questions
concerning algorithm design. These include acceleration strategies, convergence analysis with
complex costs and inexact schemes. I will also tackle practical, massively parallel and distributed
architecture implementations. Three specific applications are targeted: super-resolution in multi-
photon microscopy in biology; on-the-fly reconstruction for 3D breast tomosynthesis in medical
imaging; and mass spectrometry data processing in chemistry.

H2020 ITN Marie Sklodowska-Curie Trade-Opt Trade-Opt

Title: Training Data-Drive Experts in Optimization

Duration: 2020 - 2024

Partners:

• CentraleSupélec, France,

• TU Braunschweig, Germany,

• Univ. Graz, Austria,

• Univ. Bucharest, Romania,

• IBS PAN, Poland,

• Univ. Louvain, Belgium.

Coordinator: Silvia Villa (Univ. Genova, Italy), local: Jean-Christophe Pesquet

Project OPIS

31

Summary: The main scientific objective of the TraDE-OPT is to derive and analyse efficient optimization
algorithms for solving data-driven problems. Applications to a broad range of social, economic,
health and urban problems are expected. Nowadays, data production explodes: data are produced
by a variety of sensors in industry, vehicles, scanners, internet and mobile devices. One of the
emerging challenges is to extract interpretable information from these data. Currently, optimization,
and especially, convex optimization, is at the core of many theoretical and algorithmic methods
underpinning solver technologies for a myriad of data driven problems.

H2020 DN Marie Sklodowska-Curie EDUCADO EDUCADO

Title: Exploring the Deep Universe by Computational Analysis of Data from Observations

Duration: 2024 - 2029

Partners:

• Instituto de Astrofísicade Canarias, Tenerife,

• Universiteit Gent, Belgium,

• Rijksuniversiteit Groningen, Netherlands,

• Universita Degli Studi di Napoli Federico II, Italy,

• Istituto Nazionale di Astrofisica, Italy,

• Universitat de Barcelona, Spain,

• Université Gustave Eiffeil, France,

• Hits GGBMH, Germany

Coordinator: Yohan Knapen (Instituto de Astrofísicade Canarias, Tenerife), local: Hugues Talbot

Summary: The formation and evolution of massive galaxies is reasonably well understood in the context
of the successful standard ΛCDM formalism. Such simulations of cosmic evolution, however, lead
to serious challenges in the regime of the very faint galaxies, including the problems referred to
as missing satellites, too big to fail, and planes of satellite galaxies. With the massive amounts
of excellent data being produced by astronomical surveys, and with new missions scheduled to
produce more data of even better quality, we have a unique chance to solve these problems. To
do this, we require innovative developments in information technology. In EDUCADO (Exploring
the Deep Universe by Computational Analysis of Data from Observations), an intensive collab-
oration at the intersection of astronomy and computer science, we bring together experts from
different disciplines and sectors. We will train 10 Doctoral Candidates in the development of a
variety of high-quality methods, needed to address the formation of the faintest structures. We
will reliably and reproducibly detect unprecedented numbers of the faintest observable galaxies
from new large-area surveys. We will study the morphology, populations, and distribution of large
samples of various classes of dwarf galaxies and compare dwarf galaxy populations and properties
across different environments. We will confront the results with cosmological models of galaxy
formation and evolution. Finally, we will perform detailed, principled, and robust simulations
and observations of the Milky Way and the Local Group to compare with dwarf galaxies in other
environments. EDUCADO will deliver a comprehensive interdisciplinary, intersectoral, and inter-
national training programme including a secondment at one of our 11 associated partners for each
DC. We will provide a fresh and sustainable way of training PhD scientists with interdisciplinary
and intersectoral data science expertise, a requisite for future European competitiveness.

10.4 National initiatives

10.4.1 ANR

• Program: Chaire IA - ANR

– Project acronym: Bridgeable

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Inria Annual Report 2023

– Project title: BRIDinG thE gAp Between iterative proximaL methods and nEural networks

– Duration: 2020-2024

– Coordinator: Jean-Christophe Pesquet

• Program: ANR PRCE

– Project acronym: R-Vessel-X

– Project title: Extraction et interprétation robustes des réseaux vasculaires dans les images

biomédicales hépatiques

– Duration: 2018-2022

– Coordinator: A. Vacavant (Univ. Clermont Auvergne), local: Hugues Talbot

• Program: ANR JCJC

– Project acronym: Hagnodice

– Project title: Holistic explainable artificial intelligence schemes for lung cancer prognosis

– Duration: 2022-2026

– Coordinator: Maria Vakalopoulou

• Program: ANR JCJC

– Project acronym: GraphIA

– Project title: Scalable and robust representation learning on graphs

– Duration: 2021-2025

– Coordinator: Fragkiskos Malliaros

10.4.2 Others

PRISM Participants: Hugues Talbot, Loïc Le Bescond, Maria Vakalopoulou (Collaboration: S. Christo-
doulidis, P.-H. Cournède, MICS, CentraleSupélec ; F. André, Gustave-Roussy)

The PRISM program at Gustave-Roussy, which is a major research program on precision medicine, has
been funded by the ANR since 2018 by a grant of €5 millions. This was considered a major achievement
for the team, as it has allowed us to continue our research on the use of AI for precision medicine.

It has now received Institut Hospitalo-Universitaire (IHU) label. The vision of the project is trans-
formative in its approach for cancer treatment. It aims to better understand the biology of each patient’s
cancer and to identify, from diagnosis, those with the most aggressive tumours in order to offer them
the most appropriate treatment. This IHU label is part of the perspective of making Gustave Roussy the
largest campus in Europe dedicated to cancer.

The PRISM program has become one of the 5 IHU endowed with 30 to 40 million euros announced
by the Government as part of the 3rd call for projects of the France 2030 plan. The objective of the
IHUs is to strengthen French medical research capacity by developing world-class research (clinical and
translational) skills involving university, health establishment, research organizations and companies.

PRISM is the result of several years of research conducted by the teams of Gustave Roussy in partner-

ship with CentraleSupélec, Université Paris-Saclay, Inserm and Unicancer.

11 Dissemination

11.1 Promoting scientific activities

11.1.1 Scientific events: organisation

Member of the organizing committees

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• Emilie Chouzenoux. Co-organizer of the workshop Mathematical Fundations of AI, 2 Oct. 2023,

Paris.

• Emilie Chouzenoux. Co-organizer of the satellite program at Isaac Newton Institute on Heavy tails

in machine learning, London, UK, Apr. 2024.

• Emilie Chouzenoux: Co-organizer of a special session at IEEE International Conference on Acous-

tics, Speech and Signal Processing (ICASSP) in 2023, and in 2024.

• Emilie Chouzenoux: Co-organizer of a special session at European Signal Processing Conference

(EUSIPCO) in 2023.

• Alexandre Duval: Co-organizer of the Learning-on-Graphs (LoG) conference, Nov. 2023.

• Fragkiskos Malliaros. Co-organizer of the Graph Data Science and Applications (GraDSI) special

session at IEEE DSAA 2023, Thessaloniki, Greece.

• Hugues Talbot. Organisation Chair for International Conference on Computer Vision (ICCV), Oct.

2023, Paris.

• Hugues Talbot. Member of the organizing committee for the 2022 and 2023 Société Francophone

de Radiology challenge in Sep. 2022 and Sep. 2023.

Organization of Data Challenges Data challenges are a major way by which AI results can be publicized
and become visible. Theodore Aouad, Younes Belkouchi, Hugues Talbot, participated in organizing,
validating and evaluating the results during the 2022 and 2023 Société Francophone de Radiology chal-
lenge in September 2022 [2] and September 2023. This resulted in major review publications for the 2021
challenge [10], the 2022 challenge [14]. The 2023 challenge publication is currently being written.

11.1.2 Scientific events: selection

Chair of conference program committees

• Emilie Chouzenoux: Technical program co-chair at MLSP conference 2024, London, UK.

• Fragkiskos Malliaros: Area chair at the European Conference onMachine Learning and Principles

and Practice of Knowledge Discovery in Databases (ECML PKDD), 2021-2023.

• Nora Ouzir: Students Activities Chair for European Signal Processing Conference (EUSIPCO)

conference, Sep. 2023, Helsinki, Finland.

• Hugues Talbot: Program Committee (PC) member on the Discrete Geometry and Mathematical

Morphology conference 2024 to be held in Florence, Italy.

• Maria Vakalopoulou: Area Chair at Computer Vision and Pattern Recognition (CVPR) 2023, European
Computer Vision and Pattern Recognition (ECCV) 2023, Winter Conference on Applications of Com-
puter Vision (WACV) 2023, and Medical Image Computing and Computer Assisted Intervention
(MICCAI) 2023.

Member of the conference program committees

• Mounir Kaaniche: Tutorial Co-Chair at the European Workshop on Visual Information Processing

(EUVIP), Sept. 2023, Gjøvik, Norway.

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Inria Annual Report 2023

Reviewer The members of the team reviewed numerous papers for several international conferences,
such as for the annual conferences on Computer Vision and Pattern Recognition (CVPR),Medical Im-
age Computing and Computer Assisted Intervention (MICCAI), Neural Information Processing Systems
(NeurIPS), International Conference on Learning Representations (ICLR), IEEE International Conference
and Acoustics Speech and Signal Processing (ICASSP), IEEE International Conference on Image Pro-
cessing (ICIP), IEEE Statistical Signal Processing workshop (SSP), European Signal Processing Conference
(EUSIPCO), AAAI Conference on Artificial Intelligence (AAAI), The Web Conference (WWW), Annual
Conference of the North American Chapter of the Association for Computational Linguistics (NAACL),
International Conference on Web and Social Media (ICWSM), International Conference on Machine
Learning (ICML), Conference on Neural Information Processing Systems (NeurIPS), International Con-
ference on Complex Networks and Their Applications (Complex Networks), InternationalWorkshop on
Graph-Based Natural Language Processing (TextGraphs), Artificial Intelligence and Statistics Conference
(AIStat), British Machine Vision Conference, Montreal AI Symposium, ACM SIGKDD Conference on
Knowledge Discovery and Data Mining (KDD), IEEE/ACM International Conference on Advances in
Social Networks Analysis andMining (ASONAM), Learning on Graphs Conference (LoG).

11.1.3 Journal

Member of the editorial boards

• Emilie Chouzenoux: Associate Editor of the SIAM Journal on Imaging Sciences.

• Emilie Chouzenoux: Associate Editor of the SIAM Journal on Mathematics of Data Sciences.

• Mounir Kaaniche: Associate Editor in Frontiers in Imaging, Imaging Coding section.

• Nora Ouzir: Managing Guest Editor for the special issue EUSIPCO 2023: 31st European Signal

Processing Conference - 3 Minutes Thesis (3MT) contest collection, Science Talks, Elsevier.

• Jean-Christophe Pesquet: Associate Editor for SIAM Journal on Imaging Sciences.

• Maria Vakalopoulou: Editor at Computer Vision and Image Understanding (CVIU) Journal.

• Hugues Talbot: Review Editor for Frontiers in Computer Vision, Nature group.

• Maria Vakalopoulou: Editor at IEEE Medical Image Analysis Journal.

Reviewer - reviewing activities

• Emilie Chouzenoux: IEEE Transactions on Signal Processing, IEEE Transactions on Computational

Imaging, IEEE Transactions on Medical Imaging.

• Hugues Talbot: Computer Methods and Programs in Biomedicine (CMPB), Pattern Recognition

Letters, Computer Vision and Image Understanding (CVIU).

• Maria Vakalopoulou: IEEE Medical Image Analysis, IEEE Transactions on Pattern Analysis and

Machine Intelligence.

11.1.4 Invited talks

• Emilie Chouzenoux: Keynote talk, IABM congress, March 2023, Paris.

• Emilie Chouzenoux: Contributory talk, workshop ILLS - DATAIA, May 2023, Saclay.

• Emilie Chouzenoux: Seminar AgroParisTech, Oct. 2023, Saclay.

• Emilie Chouzenoux: Seminar LS2N, Dec. 2023, Nantes.

• Thomas Guilmeau: Invited talk, Jun. 2023, MOP Research Seminar, Mathematical Optimization for

Data Science Group, Saarland University, Germany.

Project OPIS

35

• Fragkiskos Malliaros: Invited talk, HEALth and THerapeutic Innovation (HEALTHI) consortium,

Univ. Paris-Saclay, Feb. 2023.

• Fragkiskos Malliaros: Invited talk, Board of European Students of Technology (BEST), Paris, April

2023.

• Jean-Christophe Pesquet: Keynote speaker at 9th International Conference on Scale Space and

Variational Methods in Computer Vision (SSVM 2023), May 2023, Sardinia.

• Jean-Christophe Pesquet: Lecturer at Plenoptima Training School, June 2023, Rennes.

• Fernando Marcelo Roldan Contreras: Invited talk, workshop Deep learning, image analysis, inverse

problems, and optimization (DIPOpt), Nov. 2023, Lyon.

• Tony Silveti-Falls: Invited talk, workshop Differentiating Nonsmooth Solutions to Parametric

Monotone Inclusion Problems (ICIAM), Aug. 2023, Tokyo, Japan.

• Tony Silveti-Falls: Seminar ENS Lyon, Apr. 2023, Lyon.

• Tony Silveti-Falls: Seminar at CDS, ENS ULM, June 2023, Paris.

• Tony Silveti-Falls: Invited talk,SIAM Conference on Optimization (SIOP), June 2023, Philadelphia,

USA.

• Hugues Talbot: Invited talk, Kremlin-Bicetre hospital, Jan. 2023, Créteil.

• Hugues Talbot: Invited talk, International Symposium on Continuum Models and Discrete Systems,

Jun. 2023, Paris.

• Hugues Talbot: Invited talk, 18th colloquium of the Société Française des Microscopies, July 2023,

Rouen.

• Hugues Talbot: Invited talk, Laboratoire de Mécanique Paris-Saclay ENS Paris-Saclay, Dec. 2022,

Saclay.

• Maria Vakalopoulou: Keynote talk, European Society for Therapeutic Radiology and Oncology

(ESTRO) congress, May 2023, Vienna, Austria.

• Maria Vakalopoulou: Invited talk,2nd Transatlantic Exchange Program, Deep Learning Methods for

Medical Imaging in Precision Oncology, May 2023, Paris.

• Maria Vakalopoulou: Invited talk,Workshop ILLS - Institut DATAIA, May 2023, Saclay.

• Maria Vakalopoulou: Invited talk,Hellenic Institute of Advanced Studies (HIAS) workshop on AI,

July 2023, Athens, Greece.

• Maria Vakalopoulou: Oral presentation at the Association pour la Recherche sur les Tumeurs de

Rein (A.R.Tu.R.) workshop, Oct. 2023, Paris.

• Maria Vakalopoulou: Invited talk,International Symposium on Innovative Radation Therapies

(INANOTHERAD), Oct. 2023, Saclay.

11.1.5 Leadership within the scientific community

• Jean-Christophe Pesquet: Senior honorary member of the Institut Universitaire de France. Fellow

of IEEE and EURASIP.

• Emilie Chouzenoux: Senior member of the IEEE. Elected member of the EURASIP Signal and Data
Analytics for Machine Learning TAC. Elected member of the Signal Processing Theory and Methods
IEEE Technical Comittee.

• Mounir Kaaniche: Senior member of the IEEE.

• Nora Ouzir: Elected member of the EURASIP Biomedical Image and Signals Analytics TAC.

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Inria Annual Report 2023

11.1.6 Scientific expertise

The members of the team participated to numerous PhD Thesis Committees, PhD “comité de suivi
individuel”, HdR Committees, recruiting Committees, and served as Grant Reviewers.

• Emilie Chouzenoux is member of the scientific committee of Inria Saclay.

• Emilie Chouzenoux is member of the executive committee of DataIA institute of University Paris

Saclay.

• Emilie Chouzenoux is member of the scientific committee of the programme Mathématiques du
Calcul Scientifique et de l’Ingénierie of the Labex Mathématiques Hadamard (Maths CSI LJH).

• Nora Ouzir is an elected member of CCUPS, Consultative Commission of Paris-Saclay University.

• Maria Vakalopoulou served as a panel member for ANR in 2023.

• Maria Vakalopoulou served as a monitor-expert for EU research grants.

11.1.7 Research administration

• Jean-Christophe Pesquet is the head of the laboratoire Centre pour la Vision Numérique (CVN),

CentraleSupélec.

• Maria Vakalopoulou is the group leader of the βiomathematics teams of the MICS CentraleSupélec.

• Maria Vakalopoulou is now affiliated with Archimedes Unit in Greece.

• Emilie Chouzenoux is member of the Executive Board of the H2020 ITNMarie Sklodowska-Curie.

11.1.8 Teaching administration

• Emilie Chouzenoux: attached professor in AI in CentraleSupélec, since sep. 2022.

• Fragkiskos Malliaros: co-director of the DSBA M.Sc.

• Fragkiskos Malliaros: head for the Data and Information Science (SDI) specialization at Centrale-

Supélec.

• Nora Ouzir: Co-coordinator of the Bachelor in Artificial Intelligence, Data and Management Sci-

ences, AIDAMS, with ESSEC Business School and CentraleSupélec.

• Jean-Christophe Pesquet: local head for the Optimization M.Sc. at CentraleSupélec.

• Jean-Christophe Pesquet: head for the main Optimization course in 2nd year of CentraleSupélec

(600 students).

• Hugues Talbot: local head of the MVA M.Sc. at CentraleSupélec.

11.2 Teaching - Supervision - Juries

11.2.1 Teaching

Several permanent members of OPIS were involved as lecturer (lec.) or lab instructors (lab), in the
following courses.

• Master: Emilie Chouzenoux. Foundations of Distributed and Large Scale Computing, 26h (lec.),

3rd year CentraleSupélec and M.Sc. MVA Paris Saclay.

• Master: Emilie Chouzenoux. Advanced Machine Learning, 18h (lec.), 3rd year CentraleSupélec.

• Bachelor: Emilie Chouzenoux. Optimization, 24h (lec.), 1st year Bachelor AIDAMS, Univ. Paris

Saclay.

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37

• Master: Fragkiskos Malliaros. Machine Learning in Network Science, 27h (lec.), Master in Data
Sciences and Business Analytics, CentraleSupélec and ESSEC Business School, M.Sc. in Artificial
Intelligence, CentraleSupélec, 3rd year data science mention, CentraleSupélec.

• Master: Fragkiskos Malliaros. Foundations of Machine Learning, 27h (lec.), Master in Data Sciences

and Business Analytics, CentraleSupélec and ESSEC Business School.

• Master: Jean-Christophe Pesquet. Introductory course on Optimization, 33h (lec.), 2nd year course,

CentraleSupélec, FR

• Master: Jean-Christophe Pesquet. Advanced course on Optimization, 10h (lec.), M.Sc. in Signal

Processing and Automatic Control, Univ. Paris- Saclay, FR

• Master: Jean-Christophe Pesquet. Introduction to Optimization, 6h (lec.), M.Sc. MVA, ENS Paris-

Saclay, FR

• Master: Jean-Christophe Pesquet. Convex Optimization Algorithms, 15h (lec.), M.Sc. in Optimiza-

tion, Univ. Paris-Saclay, FR

• Master: Hugues Talbot. High-performance computing, 12h (lec.), 2nd year course, CentraleSupélec,

FR

• Master: Hugues Talbot. Parallel computing, 20h (lec.), 2nd year course, CentraleSupélec, FR

• Master: Hugues Talbot. Introduction à la morphologie mathématique, 12h (lec.), 3rd year course,

CentraleSupélec, FR

• Master: Hugues Talbot. Modern Mathematical Morphology, 28h (lec.), 3rd year course, Centrale-

Supélec, M.Sc. MVA, ENS Paris-Saclay, FR

• Master: Hugues Talbot. Modern Mathematical Morphology, 30h (lec.), M.Sc in AI, CentraleSupélec,

FR

• Master: Hugues Talbot. Introduction to Machine Learning, 30h (lec.), MS Management of Techno-

logy, CentraleSupélec, FR

• Master: Tony Silveti-Falls. Convergence, Integration, and Probability, 16.5h (lec.) + 15h (lab), 1st

year course, CentraleSupélec, FR

• Master: Tony Silveti-Falls. Optimization for Computer Vision, 24h (lec. + lab), 3rd year course,

CentraleSupélec, FR

• Master: Tony Silveti-Falls. Partial Differential Equations, 13.5h (lab), 1st year course, Centrale-

Supélec, FR

• Master: Tony Silveti-Falls. Optimization, 15h (lab), 2nd year course, CentraleSupélec, FR

• Master: Tony Silveti-Falls. Parcours Recherche, 48h (project), 1st and 2nd year course, Centrale-

Supélec, FR

• Bachelor: Tony Silveti-Falls. Analysis, 18h (lab), First year Bachelors of Global Engineering students,

Univ. Paris Saclay, EN.

• Master: Maria Vakalopoulou. Introduction to Visual Computing, 25h (lec.), CentraleSupélec, FR.

• Master: Maria Vakalopoulou. Introduction to Deep Learning, M.Sc. in Data Sciences and Business

Analytics, 24h (lec.), CentraleSupélec and ESSEC Business School, FR.

• Master: Maria Vakalopoulou. Introduction to Deep Learning, M.Sc. in Artificial Intelligence, 24h

(lec.), CentraleSupélec, FR.

• Master: Maria Vakalopoulou. Deep Learning, M.Sc. in Vision and Machine Learning, 25h (lec.),

ENS Paris-Saclay, FR.

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Inria Annual Report 2023

• Master: Maria Vakalopoulou. Deep Learning in Medical Imaging, M.Sc. in Vision and Machine

Learning, 25h (lec.), ENS Paris-Saclay, FR.

Several students members of OPIS (Aymen Sardroui, Clement Cosserat, Jean-Baptiste Fest, Thomas Guil-

meau, Ségolène Martin, Younes Belkouchi, Theodore Aouad, Alexandre Duval, Loïc Le Bescond) have
teaching assistant activities, in the following cursus of the Univ. Paris Saclay campus:

• ENSTA ParisTech

• IUT Paris Saclay

• CentraleSupélec

• M.Sc. DSBA, Univ. Paris Saclay

• M.Sc. MVA, Univ. Paris Saclay

11.2.2 PhD supervision

• PhD (completed): Jean-Baptiste Fest.Stochastic Majorization-Minimization algorithms, 2020-2023,

supervised by Emilie Chouzenoux.

• PhD (completed): Ana Neacsu. Méthodes d’apprentissage profond inspirées d’algorithmes de
traitement du signal, 2019-2023, supervised by Jean-Christophe Pesquet and C. Burileanu (Po-
litehnica Bucarest).

• PhD (completed): Sagar Verma. Modélisation, contrôle et supervision de moteurs électriques par
réseaux de neurones profonds, 2019-2023, supervised by M. Castella and Jean-Christophe Pesquet.

• PhD (completed): Tassnim Dardouri. Towards a New Generation of Lifting-Based Image Coders
Using Neural Networks, 2019-2023, supervised by Mounir Kaaniche and G. Dauphin (Univ. Paris
Nord), in collaboration with Jean-Christophe Pesquet.

• PhD (completed): Kavya Gupta. Neural network solutions for safety of complex systems, 2019-2023,

supervised by Jean-Christophe Pesquet, Fragkiskos Malliaros, and F. Kaakai (Thales Group).

• PhD (completed): Maria Papadomanolaki, Change Detection from Multitemporal High Resolu-
tion Data with Deep Learning, 2017-2022, supervised by Maria Vakalopoulou and K. Karantzalos
(National Technical University of Athens).

• PhD (completed): Leo Milescki, Domain adaptation and self supervised methods for kidney trans-
plantation, 2019-2023, supervised by Maria Vakalopoulou and M.O. Timsit (Hôpital Européen
Georges-Pompidou).

• PhD (completed): Marvin Lerousseau. Apprentissage statistique en imagerie médicale et en
génomique pour prédire l’efficacité des thérapies anti-tumorales, 2018-2022, supervised by N.
Paragios (Therapanacea), E. Deutsch (IGR) and Hugues Talbot.

• PhD (completed): Georgios Panagopoulos, Influence maximization in social networks, 2018-2022,

supervised by Fragkiskos Malliaros and M. Vazirgiannis (Ècole Polytechnique).

• PhD (completed): Surabhi Jagtap, Graph-based learning from multi-omics data, 2019-2022, super-
vised by Fragkiskos Malliaros, Jean-Christophe Pesquet, and L. Duval (IFP Energies Nouvelles).

• PhD (in progress): Gabriele Scrivanti. New algorithms for large scale problems exploiting geometric
problem structure and convex relaxation, 2020-2023, supervised by Jean-Christophe Pesquet and E.
Bednarczuk (Polish Academy of Sciences, Warsaw).

• PhD (in progress): Ségolène Martin. Majorization-Minimization methods for constrained op-
timization, 2020-2023, supervised by Jean-Christophe Pesquet and I. Ben Ayed (ETS Montréal,
Canada).

Project OPIS

39

• PhD (completed): Marie-Charlotte Poilpre: Méthode de comparaison faciale morphologique, ad-
aptée aux expertise judiciaires, basée sur la modélisation 3D, 2017-2023, supervised by Hugues Tal-
bot and V. Nozick (Univ. Paris-Est).

• PhD (in progress): Theodore Aouad. Geometric semi-supervised lachine-learning methods in

medical imaging, 2020-2023, supervised by Hugues Talbot.

• PhD (in progress): Younes Belkouchi. Graph neural network generative methods in oncology,

2020-2023, supervised by N. Lassau (IGR), Fragkiskos Malliaros, and Hugues Talbot.

• PhD (in progress): Thomas Guilmeau. Algorithmes stochastiques pour l’optimisation non convexe,

2021-2024, supervised by Emilie Chouzenoux and V. Elvira (Univ. Edinburgh).

• PhD (in progress): Mouna Gharbi. Unfolded Majorization-Minimization algorithms, 2020-2023,

supervised by Emilie Chouzenoux and L. Duval (IFPEN).

• PhD (in progress): Nguyen Vu. Algorithmes d’assemblage pour l’apprentissage en imagerie

médicale, 2021-2024, supervised by Emilie Chouzenoux and P. Pinault (ESSILOR).

• PhD (in progress): Alexandre Duval. Algorithmes d’apprentissage automatique sur des graphes: ex-
plicabilité, évolutivité et applications, 2020-2023, supervised by Fragkiskos Malliaros and Hugues Tal-
bot

• PhD (in progress): Loïc Le Bescond. Precision medicine, Histology and Deep learning, 2021-2024,

supervised by F. André (IGR) and Hugues Talbot.

• PhD (in progress): Raaja El Hamdani. Robust graph representation learning and applications in
misinformation detection, 2021-2024, supervised by Fragkiskos Malliaros and T. Bonald (Télécom-
Paris).

• PhD (in progress): Othmane Laousy, Graph-based artificial intelligence methods for medical image
diagnosis, 2019-2024, supervised by Maria Vakalopoulou and M.-P. Revel (AP-HP Hospital Cochin).

• PhD (in progress): Clement Cosserat, Algorithmes de majoration-minimisation pour le traitement
du signal statistique, 2022-2025, supervised by Emilie Chouzenoux and T. Adali (Univ. Baltimore,
USA).

• PhD (in progress): Alix Chazottes, Algorithmes d’optimisation dépliés pour la reconstruction
d’images à partir de données TEP dynamique, 2023-2026, supervised by Emilie Chouzenoux and F.
Sureau (CEA, Biomaps).

• PhD (in progress): Aymen Sardroui, histopathological image analysis, 2022-2025, supervised by

Mounir Kaaniche and Jean-Christophe Pesquet.

• PhD (in progress): Arsene Amoya, neural networks-based stereo image retrieval, supervised by

Mounir Kaaniche and A. Benazza-Benyahia (SUP’COM-Tunis).

• PhD (in progress): Nabil Mouadden, Deep Learning methods for lung applications, 2023-2026,

supervised by Maria Vakalopoulou and G. Chassagnon (Hôpital Cochin).

• PhD (in progress): Jinqwei Zhang, Deep Learning methods on Digital Pathology, 2020-2024, super-

vised by Maria Vakalopoulou, and D. Samaras (Stony Brook University).

• PhD (in progress): Yassine Abbahaddou, Topics in Geometric Deep Learning, 2022-2025, supervised

by Fragkiskos Malliaros, J. Lutzeyer, and M. Vazirgiannis (École Polytechnique).

• PhD (in progress): Vahan Martirosyan, Deep Graph Neural Networks (GNNs) and Applications
in Biomedicine, 2023-2026, supervised by Fragkiskos Malliaros, Hugues Talbot, and J. Giraldo
(Télécom Paris).

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Inria Annual Report 2023

• PhD (in progress): Nicolas Salvy, Génération d’images cérébrales fonctionnelles à grande échelle
poru améliorer la cartographie cérébrale, 2023-2026, supervised by Hugues Talbot, and Bertrand
Thirion (Inria Saclay, MIND).

• PhD (in progress): Hafsa El Herichi, Extraction du tissage 3D des pièces composites de grandes
dimensions à partir d’images tomographiques, 2023-2026, supervised by Hugues Talbot, and
Stéphane Roux (Laboratoire de Mécanique de Paris-Saclay, ENS Paris-Saclay).

11.2.3 Intern supervision

• Alix Chazottes, May-Oct. 2023, supervised by Emilie Chouzenoux and Quentin Vanderbecq (Hôpital

St Joseph)

• Vuk Ignjatovic, Mar.-Sep. 2023, supervised by Nora Ouzir

• Théau Blanchard, May-Oct. 2023, supervised by Nora Ouzir

• Nicolas Dunou, May-Sep. 2023, supervised by Fragkiskos Malliaros

• Jean-Luc Dupire, June-Aug. 2023, supervised by Hugues Talbot

• Vahan Martirosyan, May-Sep. 2023, supervised by Fragkiskos Malliaros

• Corentin Pla, June-Oct. 2023, supervised by Jean-Christophe Pesquet

• Antoine Siraudin, June-Nov. 2023, supervised by Fragkiskos Malliaros

• Léo Stepien, June-Nov. 2023, supervised by Léo Stepien

• Roxane Housny, Apr.-June 2023, supervised by Aymen Sardroui and Jean-Christophe Pesquet

• Eliott Barbot, May-Oct. 2023, supervised by Ségolène Martin, Aymen Sardroui, and Jean-Christophe Pes-

quet

• Mahuton Hugues Midingoyi, Apr.-Sep. 2023, supervised by Thomas Guilmeau and Emilie Chouzen-

oux

11.2.4 Juries

The faculty members of the team serve regularly as a jury Member to Final Engineering Internship and
the Research Innovation Project for students of CentraleSupélec, and to Research Internship for students
of Ms.C. MVA, ENS Paris Saclay.

11.3 Popularization

11.3.1 Education

• Thomas Guilmeau has coordinated an animation at La fête de la science, Oct. 2023, Saclay.

• Ségolène Martin has coordinated a group of scholars at Rendez-vous des jeunes mathématiciennes

et informaticiennes (RJMI), Feb. 2023, Saclay.

• Alexandre Duval has organized several local meetups in Paris about Graph Machine Learning.

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41

12 Scientific production

12.1 Publications of the year

International journals

[1] A. Achim, L. Calatroni, S. Morigi and G. Scrivanti. ‘Space-Variant Image Reconstruction via Cauchy
Regularisation: Application to Optical Coherence Tomography’. In: Signal Processing 205 (1st Apr.
2023). DOI: 10.1016/j.sigpro.2022.108866. URL: https://hal.science/hal-03594202.

[2] Y. Belkouchi, M. Lederlin, A. Ben Afia, C. Fabre, G. R. Ferretti, C. de Margerie, P. Berge, R. Liberge,
N. Elbaz, M. Blain, P. Y. Brillet, G. Chassagnon, F. Cadour, C. Caramella, M. E. Hajjam, S. Boussouar,
J. Hadchiti, X. Fablet, A. M. Khalil, A. Luciani, A. Cotten, J. F. Meder, H. Talbot and N. Lassau.
‘Detection and quantification of pulmonary embolism with artificial intelligence: The SFR 2022
artificial intelligence data challenge’. In: Diagnostic and Interventional Imaging 104.10 (2023),
pp. 485–489. DOI: 10.1016/j.diii.2023.05.007. URL: https://hal.science/hal-0427127
2.

[3] Y. Belkouchi, H. Talbot, N. Lassau, L. Lawrance, S. Farhane, R. Feki-Mkaouar, J. Hadchiti, L. Dawi,
J. Vibert, P.-H. Cournède, C. Cousteix, C. Mazza, M. Kind, A. Italiano, A. Marabelle, S. Ammari
and S. Champiat. ‘Better than RECIST and Faster than iRECIST: Defining the Immunotherapy
Progression Decision Score to Better Manage Progressive Tumors on Immunotherapy’. In: Clinical
Cancer Research 29.8 (14th Apr. 2023), pp. 1528–1534. DOI: 10.1158/1078-0432.CCR-22-0890.
URL: https://hal.science/hal-04396436.

[4]

J. Bolte, E. Pauwels and A. J. Silveti-Falls. ‘Differentiating Nonsmooth Solutions to Parametric
Monotone Inclusion Problems’. In: SIAM Journal on Optimization 34.1 (4th Jan. 2024), 27 p. DOI:
10.1137/22M1541630. URL: https://hal.science/hal-03900339.

[5] A. Bordner, T. Aouad, C. L. Medina, S. Yang, A. Molto, H. Talbot, M. Dougados and A. Feydy. ‘A
deep learning model for the diagnosis of sacroiliitis according to Assessment of SpondyloArthritis
International Society classification criteria with magnetic resonance imaging’. In: Diagnostic and
Interventional Imaging (Apr. 2023), pp. 1–11. DOI: 10.1016/j.diii.2023.03.008. URL: https:
//cnam.hal.science/hal-04083189.

[6] M. Chalvidal, E. Chouzenoux, J.-B. Fest and C. Lefort. ‘Block delayed Majorize-Minimize subspace
algorithm for large scale image restoration’. In: Inverse Problems. Special Issue on Optimisation
and Learning Methods for Inverse Problems in Microscopy 39.4 (9th Mar. 2023), p. 044002. DOI:
10.1088/1361-6420/acbdb9. URL: https://inria.hal.science/hal-04281658.

[7] S. Chounta, R. S. Allodji, M. Vakalopoulou, M. Bentriou, D. T. Do, F. de Vathaire, I. S. Diallo, B. C.
Fresneau, T. Charrier, V. Zossou, S. Christodoulidis, S. Lemler and V. Letort Le Chevalier. ‘Dosiomics-
Based Prediction of Radiation-Induced Valvulopathy after Childhood Cancer’. In: Cancers 15.12
(2023). DOI: 10.3390/cancers15123107. URL: https://hal.science/hal-04191649.

[8] E. Chouzenoux, A. Contreras, J.-C. Pesquet and M. Savanier. ‘Convergence Results for Primal-
Dual Algorithms in the Presence of Adjoint Mismatch’. In: SIAM Journal on Imaging Sciences 16.1
(31st Mar. 2023), pp. 1–34. DOI: 10.1137/22M1490223. URL: https://inria.hal.science/hal-
04210972.

[9] E. Chouzenoux and J.-B. Fest. ‘Convergence Analysis of Block Majorize-Minimize Subspace Ap-
proach’. In: Optimization Letters (2023). DOI: 10.1007/s11590-023-02055-z. URL: https://in
ria.hal.science/hal-04202728.

[10] V. Couteaux, C. Zhang, S. Mulé, L. Milot, P.-J. Valette, C. Raynaud, A. S. Vlachomitrou, C. Ciofolo-
Veit, L. Lawrance, Y. Belkouchi, V. Vilgrain, M. Lewin, H. Trillaud, C. Hoeffel, V. Laurent, S. Ammari,
E. Morand, O. Faucoz, A. Tenenhaus, H. Talbot, A. Luciani, N. Lassau and C. Lazarus. ‘Synthetic
MR image generation of macrotrabecular-massive hepatocellular carcinoma using generative
adversarial networks’. In: Diagnostic and Interventional Imaging 104.5 (2023), pp. 243–247. DOI:
10.1016/j.diii.2023.01.003. URL: https://hal.science/hal-03961793.

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Inria Annual Report 2023

[11] T. Dardouri, M. Kaaniche, A. Benazza-Benyahia, G. Dauphin and J.-C. Pesquet. ‘Joint Learning of
Fully Connected Network Models in Lifting Based Image Coders’. In: IEEE Transactions on Image
Processing 33 (2023), pp. 134–148. DOI: 10.1109/TIP.2023.3333279. URL: https://hal.scien
ce/hal-04372168.

[12] P. Decazes, S. Ammari, Y. Belkouchi, L. Mottay, L. Lawrance, A. de Prévia, H. Talbot, S. Farhane,
P.-H. P.-H. Cournède, A. Marabelle, F. Guisier, D. Planchard, T. Ibrahim, C. Robert, F. Barlesi, P. Vera
and N. Lassau. ‘Synergic prognostic value of 3D CT scan subcutaneous fat and muscle masses for
immunotherapy-treated cancer’. In: Journal for Immunotherapy of Cancer 11.9 (7th Sept. 2023),
e007315. DOI: 10.1136/jitc-2023-007315. URL: https://hal.science/hal-04287256.

[13] C. Delmon, E. Ferrandon, E. Chouzenoux, A. Prorot, S. Alain and C. Lefort. ‘A computational two-
photon fluorescence approach for revealing label-free the 3D image of viruses and bacteria’. In:
Journal of Biophotonics 16.5 (2023), p. 9. DOI: 10.1002/jbio.202200266. URL: https://hal.sc
ience/hal-03925176.

[14] A. Djahnine, C. Lazarus, M. Lederlin, S. Mulé, R. Wiemker, S. Si-Mohamed, E. Jupin-Delevaux, O.
Nempont, Y. Skandarani, M. de Craene, S. Goubalan, C. Raynaud, Y. Belkouchi, A. B. Afia, C. Fabre,
G. Ferretti, C. de Margerie, P. Berge, R. Liberge, N. Elbaz, M. Blain, P.-Y. Brillet, G. Chassagnon,
F. Cadour, C. Caramella, M. E. Hajjam, S. Boussouar, J. Hadchiti, X. Fablet, A. Khalil, H. Talbot, A.
Luciani, N. Lassau and L. Boussel. ‘Detection and severity quantification of pulmonary embolism
with 3D CT data using an automated deep learning-based artificial solution’. In: Diagnostic and
Interventional Imaging (Oct. 2023). DOI: 10.1016/j.diii.2023.09.006. URL: https://hal.sc
ience/hal-04396326.

[15] V. Elvira, E. Chouzenoux, Ö. D. Akyildiz and L. Martino. ‘Gradient-based adaptive importance
samplers’. In: Journal of The Franklin Institute 360.13 (Sept. 2023), pp. 9490–9514. DOI: 10.1016/j
.jfranklin.2023.06.041. URL: https://inria.hal.science/hal-04165344.

[16] M. Gharbi, E. Chouzenoux and J.-C. Pesquet. ‘An Unrolled Half-Quadratic Approach for Sparse
Signal Recovery in Spectroscopy’. In: Signal Processing (Dec. 2023). URL: https://inria.hal.sc
ience/hal-04229774.

[17] Y. Gu, Y. Otake, K. Uemura, M. Soufi, M. Takao, H. Talbot, S. Okada, N. Sugano and Y. Sato. ‘Bone
mineral density estimation from a plain X-ray image by learning decomposition into projections
of bone-segmented computed tomography’. In: Medical Image Analysis 90 (Dec. 2023), p. 102970.
DOI: 10.1016/j.media.2023.102970. URL: https://hal.science/hal-04396297.

[18] T. Guilmeau and A. Rapaport. ‘Multiplicity of neutrally stable periodic orbits with coexistence in the
chemostat subject to periodic removal rate’. In: SIAM Journal on Applied Mathematics 84.1 (16th Jan.
2024), pp. 39–59. DOI: 10.1137/23M1552450. URL: https://hal.inrae.fr/hal-04248438.

[19] P. Gupta, A. Majumdar, E. Chouzenoux and G. Chierchia. ‘DeConDFFuse: Predicting drug-drug
interaction using joint deep convolutional transform learning and decision forest fusion frame-
work’. In: Expert Systems with Applications (2023), p. 22. DOI: 10.1016/j.eswa.2023.120238.
URL: https://inria.hal.science/hal-04089994.

[20] Y. Huang, E. Chouzenoux, V. Elvira and J.-C. Pesquet. ‘Efficient Bayes Inference in Neural Networks
through Adaptive Importance Sampling’. In: Journal of The Franklin Institute 360.16 (Sept. 2023),
pp. 12125–12149. DOI: 10.1016/j.jfranklin.2023.08.044. URL: https://inria.hal.scien
ce/hal-04210696.

[21]

S. Jain, E. Chouzenoux, K. Kumar and A. Majumdar. ‘Graph Regularized Probabilistic Matrix Factor-
ization for Drug-Drug Interactions Prediction’. In: IEEE Journal of Biomedical and Health Informat-
ics 27.5 (May 2023), pp. 2565–2574. DOI: 10.1109/JBHI.2023.3246225. URL: https://inria.h
al.science/hal-04089977.

[22] B. Kerautret, P. Ngo, N. Passat, H. Talbot and C. Jaquet. ‘OpenCCO: An Implementation of Con-
strained Constructive Optimization for Generating 2D and 3D Vascular Trees’. In: Image Processing
On Line 13 (2023), pp. 258–279. DOI: 10.5201/ipol.2023.477. URL: https://hal.science/ha
l-04201337.

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43

[23] F. Mosele, E. Deluche, A. Lusque, L. Le Bescond, T. Filleron, Y. Pradat, A. Ducoulombier, B. Pistilli,
T. Bachelot, F. Viret, C. Levy, N. Signolle, A. Alfaro, D. T. N. Tran, I. J. Garberis, H. Talbot, S. Christo-
doulidis, M. Vakalopoulou, N. Droin, A. Stourm, M. Kobayashi, T. Kakegawa, L. Lacroix, P. Saulnier,
B. Job, M. Deloger, M. Jimenez, C. Mahier, V. Baris, P. Laplante, P. Kannouche, V. Marty, M. Lacroix-
Triki, V. Diéras and F. André. ‘Trastuzumab deruxtecan in metastatic breast cancer with variable
HER2 expression: the phase 2 DAISY trial’. In: Nature Medicine 29.8 (24th July 2023), pp. 2110–2120.
DOI: 10.1038/s41591-023-02478-2. URL: https://hal.science/hal-04187928.

[24] S. Mulé, L. Lawrance, Y. Belkouchi, V. Vilgrain, M. Lewin, H. Trillaud, C. Hoeffel, V. Laurent, S.
Ammari, E. Morand, O. Faucoz, A. Tenenhaus, A. Cotten, J.-F. Meder, H. Talbot, A. Luciani and
N. Lassau. ‘Generative adversarial networks (GAN)-based data augmentation of rare liver cancers:
The SFR 2021 Artificial Intelligence Data Challenge’. In: Diagnostic and Interventional Imaging
104.1 (2023), pp. 43–48. DOI: 10.1016/j.diii.2022.09.005. URL: https://hal.science/hal-
03907631.

[25] A.-A. Neacs, u, J.-C. Pesquet, V. Vasilescu and C. Burileanu. ‘ABBA Neural Networks: Coping with
Positivity, Expressivity, and Robustness’. In: SIAM Journal on Mathematics of Data Science (2024).
URL: https://hal.science/hal-04386260.

[26]

J.-C. Pesquet, C. Burileanu and A.-A. Neacs, u. ‘EMG-Based Automatic Gesture Recognition Using
Lipschitz-Regularized Neural Networks’. In: ACM Transactions on Intelligent Systems and Techno-
logy (8th Dec. 2023). URL: https://hal.science/hal-03751766.

[27] M. Savanier, E. Chouzenoux, J.-C. Pesquet and C. Riddell. ‘Deep Unfolding of the DBFB Algorithm
With Application to ROI CT Imaging With Limited Angular Density’. In: IEEE Transactions on
Computational Imaging 9 (2023), pp. 502–516. DOI: 10.1109/TCI.2023.3279053. URL: https:
//inria.hal.science/hal-04210704.

[28] S. Sharma, A. Majumdar, E. Chouzenoux and V. Elvira. ‘Deep State-Space Model for Predicting
Cryptocurrency Price’. In: Information Sciences (21st Nov. 2023). URL: https://inria.hal.scie
nce/hal-04358461.

[29] S. Verma, N. Henwood, M. Castella, A. K. Jebai and J.-C. Pesquet. ‘Neural speed–torque estimator
for induction motors in the presence of measurement noise’. In: IEEE Transactions on Industrial
Electronics 70.1 (Jan. 2023), pp. 167–177. DOI: 10.1109/tie.2022.3153830. URL: https://hal
.science/hal-03887116.

[30] P. Zheng, E. Chouzenoux and L. Duval. ‘PENDANTSS: PEnalized Norm-ratios Disentangling Additive
Noise, Trend and Sparse Spikes’. In: IEEE Signal Processing Letters 30 (1st Mar. 2023), pp. 215–219.
DOI: 10.1109/LSP.2023.3251891/mm1. URL: https://hal.science/hal-03924136.

Invited conferences

[31] V. Elvira, E. Chouzenoux, J. Cerda and G. Camps-Valls. ‘Graphs in State-Space Models for Granger
Causality in Climate Science’. In: Colloquium 2023 - When Causal Inference Meets Statistical
Analysis. Paris, France, 17th Apr. 2023. URL: https://inria.hal.science/hal-04165347.

[32] O. Laousy, A. Araujo, G. Chassagnon, M.-P. Revel, S. Garg, F. Khorrami and M. Vakalopoulou.
‘Towards Better Certified Segmentation via Diffusion Models’. In: Proceedings of Machine Learning
Research. UAI 2023 - The 39th Conference on Uncertainty in Artificial Intelligence -. Vol. 216. 112.
Pittsburgh (Pennsylvania), United States, July 2023, pp. 1185–1195. URL: https://hal.science
/hal-04388219.

[33] C. de Valle, E. Centofanti, E. Chouzenoux and J.-C. Pesquet. ‘Stability of Unfolded Forward-
Backward to Perturbations in Observed Data’. In: Proceedings of the 31st European Signal Processing
Conference (EUSIPCO 2023). EUSIPCO 2023 - 31st European Signal Processing Conference. Helsinki,
Finland, 4th Sept. 2023. URL: https://inria.hal.science/hal-04165350.

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International peer-reviewed conferences

[34] T. Aouad and H. Talbot. ‘A foundation for exact binarized morphological neural networks’. In: ICCV
2023 - International Conference on Computer Vision, workshop on Low Bit Quantized Neural
Networks. Paris, France, 8th Jan. 2024. URL: https://hal.science/hal-04231040.

[35] A. Carbonero, A. Duval, V. Schmidt, S. Miret, A. Hernandez-Garcia, Y. Bengio and D. Rolnick. ‘On
the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions’. In: 37th
Conference on Neural Information Processing Systems (NeurIPS 2023) - AI4MAt workshop. New
Orleans (Louisiana), United States, Dec. 2023. URL: https://inria.hal.science/hal-044079
06.

[36]

J. A. Castro-Correa, J. H. Giraldo, A. Mondal, M. Badiey, T. Bouwmans and F. D. Malliaros. ‘Time-
varying Signals Recovery via Graph Neural Networks’. In: ICASSP 2023 - IEEE International Con-
ference on Acoustics, Speech and Signal Processing. Rhodes Island, Greece, 2023, pp. 1–5. DOI:
10.1109/ICASSP49357.2023.10096168. URL: https://hal.science/hal-04368767.

[37] E. Chouzenoux and V. Elvira. ‘GRAPHIT: Iterative reweighted l1 algorithm for sparse graph inference
in state-space models’. In: ICASSP 2023 - IEEE International Conference on Acoustics, Speech and
Signal Processing. Rhodes (Grèce), Greece: IEEE, 2023. DOI: 10.1109/ICASSP49357.2023.10096
021. URL: https://inria.hal.science/hal-04094496.

[38] C. Cosserat, B. Gabrielson, E. Chouzenoux, J.-C. Pesquet and T. Adali. ‘A proximal approach to
IVA-G with convergence guarantees’. In: ICASSP 2023 - IEEE International Conference on Acoustics,
Speech and Signal Processing. Proceedings of the IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2023). Rhodes (Grèce), Greece, June 2023. DOI: 10.1109
/ICASSP49357.2023.10096421. URL: https://inria.hal.science/hal-04094479.

[39] A. Duval, V. Schmidt, A. Hernandez, S. Miret, F. D. Malliaros, Y. Bengio and D. Rolnick. ‘FAENet:
Frame Averaging Equivariant GNN for Materials Modeling’. In: ICML 2023 - International Confer-
ence on Machine Learning. Hawaii, United States, 23rd July 2023. URL: https://hal.science/h
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[40]

[41]

[42]

J.-B. Fest, A. Repetti and E. Chouzenoux. ‘A new non-convex framework to improve asymptot-
ical knowledge on generic stochastic gradient descent’. In: Proceedings of the IEEE International
Workshop on Machine Learning for Signal Processing (MLSP 2023). MLSP 2023 - IEEE Interna-
tional Workshop on Machine Learning for Signal Processing. Rome, Italy, 17th Sept. 2023. URL:
https://inria.hal.science/hal-04165342.

J. H. Giraldo, K. Skianis, T. Bouwmans and F. D. Malliaros. ‘On the Trade-off between Over-smoothing
and Over-squashing in Deep Graph Neural Networks’. In: CIKM 2023 - The 32nd ACM Interna-
tional Conference on Information and Knowledge Management. Birmingham United Kingdom,
United Kingdom: ACM, 21st Oct. 2023, pp. 566–576. DOI: 10 . 1145 / 3583780 . 3614997. URL:
https://hal.science/hal-04263891.

J. H. Giraldo, S. Javed, A. Mahmood, F. D. Malliaros and T. Bouwmans. ‘Higher-order Sparse Con-
volutions in Graph Neural Networks’. In: ICASSP 2023 - 2023 IEEE International Conference on
Acoustics, Speech, and Signal Processing. Rhodes Island, Greece: IEEE, 2023, pp. 1–5. DOI: 10.1109
/ICASSP49357.2023.10096494. URL: https://hal.science/hal-04368752.

[43] Y. Gu, Y. Otake, K. Uemura, M. Takao, M. Soufi, Y. Hiasa, H. Talbot, S. Okada, N. Sugano and Y. Sato.
‘MSKdeX: Musculoskeletal (MSK) Decomposition from an X-Ray Image for Fine-Grained Estimation
of Lean Muscle Mass and Muscle Volume’. In: Lecture Notes in Computer Science. MICCAI 2023 -
26th International Conference on Medical Image Computing and Computer-Assisted Intervention.
Vol. LNCS-14226. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023.
Vancoucer, Canada: Springer Nature Switzerland, 1st Oct. 2023, pp. 497–507. DOI: 10.1007/978-3
-031-43990-2_47. URL: https://hal.science/hal-04396407.

[44] T. Guilmeau, E. Chouzenoux and V. Elvira. ‘Adaptive simulated annealing through alternating Renyi
divergence minimization’. In: ICASSP 2023 - IEEE International Conference on Acoustics, Speech
and Signal Processing. Proceedings of the IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP 2023). Rhodes (Grèce), Greece, 2023. DOI: 10.1109/ICASSP49357.202
3.10096619. URL: https://inria.hal.science/hal-04094489.

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[45] A. Hernandez-Garcia, A. Duval, A. Volokhova, Y. Bengio, D. Sharma, P. L. Carrier, M. Koziarski
and V. Schmidt. ‘Crystal-GFN: sampling crystals with desirable properties and constraints’. In:
37th Conference on Neural Information Processing Systems (NeurIPS 2023) - AI4MAt workshop.
Proceedings of NeurIPS 2023. New Orleans (Louisiana), United States, Dec. 2023. URL: https://i
nria.hal.science/hal-04407903.

[46] H. Jain, S. Verma and S. Gupta. ‘Investigating Large Vision Model Training Challenges on Satellite
Datasets’. In: InGARSS 2023 - India Geoscience and Remote Sensing Symposium. Bengaluru, India,
10th Dec. 2023. URL: https://hal.science/hal-04231035.

[47] O. Laousy, A. Araujo, G. Chassagnon, N. Paragios, M. P. Revel and M. Vakalopoulou. ‘Certification of
Deep Learning Models for Medical Image Segmentation’. In: Lecture Notes in Computer Science
book series (LNCS,volume 14223). MICCAI 2023 - 26th International Conference on Medical Image
Computing and Computer Assisted Intervention. Vol. 14223. Vancouver (BC), Canada, 1st Oct. 2023,
pp. 611–621. DOI: 10.1007/978-3-031-43901-8_58. URL: https://hal.science/hal-04388
114.

[48] S. Martinot, N. Komodakis, M. Vakalopoulou, N. Bus, C. Robert, E. Deutsch and N. Paragios.
‘Differentiable Gamma Index-Based Loss Functions: Accelerating Monte-Carlo Radiotherapy Dose
Simulation’. In: Information Processing in Medical Imaging. Vol. 13939. Lecture Notes in Computer
Science. San Carlos De Bariloche, Argentina: Springer Nature Switzerland, 8th June 2023, pp. 485–
496. DOI: 10.1007/978-3-031-34048-2_37. URL: https://hal.science/hal-04392320.

[49] L. Milecki, V. Kalogeiton, S. Bodard, D. Anglicheau, J.-M. Correas, M.-O. Timsit and M. Vakalo-
poulou. ‘MEDIMP: 3D Medical Images with clinical Prompts from limited tabular data for renal
transplantation’. In: Proceedings of Machine Learning Research. MIDL 2023 - Medical Imaging with
Deep Learning. Nashville (Tennessee), United States, 10th July 2023. URL: https://centralesup
elec.hal.science/hal-04040697.

[50]

J. Nicolaes, E. Tselenti, T. Aouad, C. López-Medina, A. Feydy, H. Talbot, B. Hoepken, N. de Peyrecave
and M. Dougados. ‘POS0341 PERFORMANCE ANALYSIS OF A DEEP LEARNING ALGORITHM TO
DETECT POSITIVE SIJ MRI ACCORDING TO THE ASAS DEFINITION IN AXSPA PATIENTS’. In:
EULAR 2023 European Congress of Rheumatology, Milan, Italy: BMJ Publishing Group Ltd and
European League Against Rheumatism, 31st May 2023, pp. 418.1–418. DOI: 10.1136/annrheumdi
s-2023-eular.2357. URL: https://hal.science/hal-04402690.

[51] G. Panagopoulos, N. Tziortziotis, M. Vazirgiannis and F. D. Malliaros. ‘Maximizing Influence with
Graph Neural Networks’. In: ASONAM 2023 - IEEE/ACM International Conference on Advances in
Social Networks Analysis and Mining. Kusadasi, Turkey, 6th Nov. 2023. URL: https://hal.scienc
e/hal-04368762.

[52] A. Panigrahi, S. Verma, M. Terris and M. Vakalopoulou. ‘Have Foundational Models Seen Satellite
Images?’ In: IGARSS 2023 - International Geoscience and Remote Sensing Symposium. Pasadena,
United States, 31st May 2023. URL: https://hal.science/hal-04112634.

[53]

J. Rony, J.-C. Pesquet and I. B. Ayed. ‘Proximal Splitting Adversarial Attack for Semantic Segmenta-
tion’. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Van-
couver (CA), Canada: IEEE, 2023, pp. 20524–20533. DOI: 10.1109/CVPR52729.2023.01966. URL:
https://hal.science/hal-04382554.

[54] R. Sallé de Chou, M. A. Srir, L. Najman, N. Passat, H. Talbot and I. Vignon-Clementel. ‘Convex
optimization for binary tree-based transport networks’. In: International Conference on Discrete
Geometry and Mathematical Morphology (DGMM). Florence, Italy, 2024. URL: https://inria.h
al.science/hal-04359833.

[55] M. Terris and S. Verma. ‘Investigating Model Robustness Against Sensor Variation’. In: IGARSS 2023
- International Geoscience and Remote Sensing Symposium. Pasadena, United States, 16th July
2023. URL: https://hal.science/hal-04112635.

[56] S. Verma and K. Gupta. ‘Post Wildfire Burnt-up Detection using Siamese UNet’. In: ECML PKDD
2023 - European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases. Turin, Italy, 18th Sept. 2023. URL: https://hal.science/hal-0422547
4.

46

[57]

[58]

Inria Annual Report 2023

J. Zhang, S. Kapse, K. Ma, P. Prasanna, J. Saltz, M. Vakalopoulou and D. Samaras. ‘Prompt-MIL:
Boosting Multi-instance Learning Schemes via Task-Specific Prompt Tuning’. In: Lecture Notes in
Computer Science. MICCAI 2023 - 26th International Conference on Medical Image Computing
and Computer Assisted Intervention. Vol. LNCS-14227. Medical Image Computing and Computer
Assisted Intervention – MICCAI 2023. Vancouver, Canada: Springer Nature Switzerland, 1st Oct.
2023, pp. 624–634. DOI: 10.1007/978-3-031-43993-3_60. URL: https://hal.science/hal-0
4392329.

J. Zhang, K. Ma, S. Kapse, J. Saltz, M. Vakalopoulou, P. Prasanna and D. Samaras. ‘SAM-Path: A
Segment Anything Model for Semantic Segmentation in Digital Pathology’. In: Lecture Notes in
Computer Science. MICCAI 2023 - Medical Image Computing and Computer Assisted Intervention.
Vol. LNCS-14393. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023
Workshops. Vancouver, Canada: Springer Nature Switzerland, 1st Dec. 2023, pp. 161–170. DOI:
10.1007/978-3-031-47401-9_16. URL: https://hal.science/hal-04392351.

[59] P. Zheng, E. Chouzenoux and L. Duval. ‘Démélange, déconvolution et débruitage conjoints d’un
modèle convolutif parcimonieux avec dérive instrumentale, par pénalisation de rapports de normes
ou quasi-normes lissées (PENDANTSS)’. In: GRETSI 2023. Grenoble, France, 4th July 2023. URL:
https://hal.science/hal-04153328.

Conferences without proceedings

[60] Y. Belkouchi, L. Dawi, L. Lawrance, S. Ammari, D. Vasseur, F. Wirth, O. Gautier, P.-H. Cournede,
J. Hadchiti, C. David, F. Bidault, C. Balleyguier, M. Kind, H. Talbot, A. Italiano and N. Lassau.
‘142P Correlating total tumor volume on CT-Scan and liquid biopsy ctDNA in 1017 patients with
metastatic cancer: A novel study’. In: ESMO Congress 2023. Vol. 34. Madrid, Spain, Oct. 2023, S237.
DOI: 10.1016/j.annonc.2023.09.2867. URL: https://hal.science/hal-04396382.

[61] L. Dawi, S. Ammari, Y. Belkouchi, J. Hadchiti, L. Lawrance, F. Wirth, A. Bertin, S. Morer, N. Billet, C.
Balleyguier, P.-H. Cournede, H. Talbot and N. Lassau. ‘1022MO Predicting overall survival of patients
with melanoma and NSCLC treated with immunotherapy using AI combining total tumor volume
and tumor heterogeneity on CT-Scans’. In: ESMO Congress 2023. Vol. 34. Madrid, Spain, Oct. 2023,
S622. DOI: 10.1016/j.annonc.2023.09.2161. URL: https://hal.science/hal-04396366.

[62] B. Kerautret, P. Ngo, N. Passat, H. Talbot, C. Jaquet and A. Germain. ‘OpenCCO : Une implémentation
de l’algorithme CCO pour la construction d’arbres vasculaires 2D et 3D’. In: Atelier R-Vessel-X 2023.
Lyon, France, 15th June 2023. URL: https://hal.science/hal-04133361.

[63] A. Quillent, S. Ammari, É. Chouzenoux, V. Elvira and N. Lassau. ‘Machine-learning based guided
diagnosis of parotid tumours from MRI’. In: IEEE International Symposium on Biomedical Imaging
(ISBI 2023). Cartagena (Colombia), Colombia, 18th Apr. 2023. URL: https://inria.hal.science
/hal-04407914.

Scientific book chapters

[64] E. Chouzenoux and J.-C. Pesquet. ‘Optimization Methods for Signal Processing’. In: Source Separa-
tion in Physical-Chemical Sensing. Chapter 2. IEEE Press; JOHN WILEY & SONS, Oct. 2023. URL:
https://inria.hal.science/hal-04250055.

Reports & preprints

[65]

J. Ajdenbaum, E. Chouzenoux, C. Lefort, S. Martin and J.-C. Pesquet. A Novel Variational Approach
for Multiphoton Microscopy Image Restoration: from PSF Estimation to 3D Deconvolution. 1st Nov.
2023. URL: https://hal.science/hal-04296247.

[66]

S. Chatterjee, A. Majumdar and E. Chouzenoux. Antibiotic Bacteria Interaction: Dataset and Bench-
marking. 6th Jan. 2024. URL: https://inria.hal.science/hal-04376907.

[67] E. Chouzenoux, M.-C. Corbineau, J.-C. Pesquet and G. Scrivanti. A variational approach for joint
image recovery and feature extraction based on spatially varying generalised Gaussian models.
11th Oct. 2023. URL: https://hal.science/hal-03591742.

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47

[68] E. Chouzenoux and V. Elvira. Sparse Graphical Linear Dynamical Systems. 2023. URL: https://inr

ia.hal.science/hal-04358490.

[69] E. Chouzenoux, J.-B. Fest and A. Repetti. A Kurdyka-Lojasiewicz property for stochastic optimization
algorithms in a non-convex setting. 13th Feb. 2023. DOI: 10.48550/arXiv.2302.06447. URL:
https://hal.inria.fr/hal-03992655.

[70] E. Chouzenoux, J.-C. Pesquet and F. Roldán. Solution of Mismatched Monotone+Lipschitz Inclusion
Problems. 2023. DOI: 10.48550/arXiv.2310.06402. URL: https://inria.hal.science/hal-
04237154.

[71] P. Gajardo, T. Guilmeau and C. Hermosilla. Sensitivity Analysis of the Set of Sustainable Thresholds.

1st June 2023. URL: https://hal.science/hal-04112730.

[72] M. Gharbi, S. Villa, E. Chouzenoux and J.-C. Pesquet. Unrolled deep networks for sparse signal

restoration. 2023. URL: https://inria.hal.science/hal-03988686.

[73] T. Guilmeau and A. Rapaport. Multiplicity of periodic orbits with coexistence in the chemostat subject

to periodic removal rate. 9th Feb. 2023. URL: https://hal.inrae.fr/hal-03982233.

[74] K. Gupta and S. Verma. CertViT: Certified Robustness of Pre-Trained Vision Transformers. 20th Jan.

2023. URL: https://inria.hal.science/hal-03966892.

[75] L. Le Bescond, M. Vakalopoulou, S. Christodoulidis, F. Andre and H. Talbot. On the detection of
Out-Of-Distribution samples in Multiple Instance Learning. 12th Dec. 2023. URL: https://hal.sc
ience/hal-04337462.

[76]

J. Nicolaes, E. Tselenti, T. Aouad, C. López-Medina, A. Feydy, H. Talbot, B. Hoepken, N. de Peyrecave
and M. Dougados. Performance Analysis of a Deep Learning Algorithm to Detect MRI Positive Sacroil-
iac Joints in Patients with Axial Spondyloarthritis According to the Assessment of SpondyloArthritis
international Society 2009 Definition. 18th Jan. 2024. URL: https://hal.science/hal-0440203
0.

[77] N. L. Ouzir, F. Pascal and J.-C. Pesquet. Convex Parameter Estimation of Perturbed Multivariate
Generalized Gaussian Distributions. 12th Dec. 2023. URL: https://hal.science/hal-04366765.

[78] A. Sadraoui, S. Martin, E. Barbot, A. Laurent-Bellue, J.-C. Pesquet, C. Guettier and I. B. Ayed. A
transductive few-shot learning approach for classification of digital histopathological slides from
liver cance. 24th Nov. 2023. URL: https://hal.science/hal-04305713.

[79]

[80]

S. Sharma, É. Chouzenoux, V. Elvira and A. Majumdar. A dynamical model for stock forecasting via
deep recurrent dictionary learning. Dec. 2023. URL: https://hal.science/hal-03654152.

S. Verma, S. Gupta and K. Gupta. Synthetix: Pipeline for Synthetic Geospatial Data Generation. 2023.
URL: https://hal.science/hal-04349391.

[81] M. Vu, E. Chouzenoux, J.-C. Pesquet and I. B. Ayed. Aggregated f -average Neural Network for
Interpretable Ensembling. 9th Oct. 2023. DOI: 10.48550/arXiv.2310.05566. URL: https://inr
ia.hal.science/hal-04237149.
<scratchpad>
- Main topic: Research Centre OPIS (Optimization for large Scale biomedical data)
- Key points:
  - Project-Team OPIS was created in 2019 May 01
  - The team is focused on developing advanced optimization methods for biomedical data analysis
  - Applications include digital health, biology, and earth sciences
  - Research areas include computational neuroscience, medicine, and imaging
  - The team is also involved in bilateral contracts and grants with industry
- Structure: The document is divided into sections, including Research Centre, Project-Team, Activity Report, and Bilateral contracts and grants with industry
- Potential artifacts to ignore: Web-related elements such as links, HTML tags, and other web-related elements
</scratchpad>

<final_summary>
The Research Centre OPIS (Optimization for large Scale biomedical data) is a project that focuses on developing advanced optimization methods for biomedical data analysis. The team, created in 2019 May 01, is involved in various research areas, including computational neuroscience, medicine, and imaging. The team's work has been recognized with several awards, including the best poster award at the Information Processing In Medical Imaging (IPMI) conference in 2023. The OPIS project has also been involved in bilateral contracts and grants with industry, including companies such as GE Healthcare and Schneider Electric. The team's research has the potential to improve the analysis and processing of large and complex biomedical data, leading to better diagnosis and treatment of diseases.
The Research Centre OPIS (Optimization for large Scale biomedical data) is a project that focuses on developing advanced optimization methods for biomedical data analysis. The team, created in 2019 May 01, is involved in various research areas, including computational neuroscience, medicine, and imaging. The team's work has been recognized with several awards, including the best poster award at the Information Processing In Medical Imaging (IPMI) conference in 2023. The OPIS project has also been involved in bilateral contracts and grants with industry, including companies such as GE Healthcare and Schneider Electric. The team's research has the potential to improve the analysis and processing of large and complex biomedical data, leading to better diagnosis and treatment of diseases.