| --- |
| task_categories: |
| - image-to-image |
| tags: |
| - medical |
| - video-super-resolution |
| - medical-imaging |
| - surgical-video |
| --- |
| |
| # MedVSR Dataset |
|
|
| [**Paper**](https://huggingface.co/papers/2509.21265) | [**GitHub**](https://github.com/CUHK-AIM-Group/MedVSR) |
|
|
| MedVSR is a framework and dataset collection tailored for medical video super-resolution (VSR). It addresses unique challenges in clinical medical videos, such as camera shake, noise, and abrupt frame transitions, which often lead to alignment errors in standard VSR models. |
|
|
| This repository contains preprocessed versions of the following medical datasets used in the study: |
| - **HyperKvasir**: Endoscopy images and videos. |
| - **LDPolyp**: A large-scale dataset for polyp detection and classification. |
| - **EndoVis18**: Data from the Endoscopic Vision Challenge. |
|
|
| These datasets cover various medical scenarios, including endoscopy and cataract surgeries, and are used to evaluate the MedVSR model's ability to selectively propagate consistent features and enhance tissue structures. |
|
|
| ## Dataset Preparation |
|
|
| According to the [official repository](https://github.com/CUHK-AIM-Group/MedVSR), users should download the data from this Hugging Face repository and update the paths in the configuration files (e.g., `options/medvsr_train.yml`) to point to the extracted folders. |
|
|
| ## Citation |
|
|
| If you find this dataset or the MedVSR framework useful for your research, please cite the following paper: |
|
|
| ```bibtex |
| @inproceedings{liu2025medvsr, |
| title = {MedVSR: Medical Video Super-Resolution with Cross State-Space Propagation}, |
| author = {Liu, Xinyu and Sun, Guolei and Wang, Cheng and Yuan, Yixuan and Konukoglu, Ender}, |
| booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, |
| year = {2025} |
| } |
| ``` |
