Update README.md

README.md

@@ -1,44 +1,7 @@
 ---
 license: cc-by-nc-4.0
 ---
-### CT-Scroll: A Global-Local Attention Model for 3D Chest CT Volumes 🩺👨🏻‍⚕️
-Official hub for the paper "Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification".
-
-Paper accepted for [MIDL](https://www.midl.io/) 2025 : [arXiv submission](https://arxiv.org/abs/2503.20652)
-
-## Method Overview
-
-The CT-Scroll architecture consists of three main components. (1) Axial slices of the volume are grouped into triplets and processed by a ResNet followed by a GAP layer, producing a vector representation per triplet. (2) The Scrolling Block then refines these embedded visual tokens using both global and local attention mechanisms. (3) Finally, the aggregated representations are fed into a classification head to predict anomalies.
-
-<img src="./figures/method_overview.png" width="1000">
-
-## Clone the Repository
-
-To clone this repository, use the following command:
-
-```bash
-git clone https://huggingface.co/theodp/ct-scroll
-```
 
 ## Code repository
 
 The project source code can be found [here](https://github.com/theodpzz/ct-scroll).
-
-## Acknowledgments
-
-We acknowledge [Hamamci et al. 2024](http://arxiv.org/abs/2403.17834) for making the [CT-RATE dataset](https://huggingface.co/datasets/ibrahimhamamci/CT-RATE) available, and [Draelos et al. 2021](https://www.sciencedirect.com/science/article/pii/S1361841520302218) for making the [Rad-ChestCT dataset](https://zenodo.org/records/6406114) available. We would like to express our gratitude to [CT-CLIP GitHub repository](https://github.com/ibrahimethemhamamci/CT-CLIP) for providing a codebase that served as a foundation for this project.
-
-## Citation
-
-> [!IMPORTANT]  
-> This project is based on the work by Di Piazza et al. If you use this code in your research, please cite the following paper:
-
-```BibTeX
-@inproceedings{dpzz2025ctscroll,
-  author    = {Di Piazza Theo, Carole Lazarus, Olivier Nempont and Loic Boussel},
-  title     = {Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification},
-  booktitle = {Proceedings of Medical Imaging with Deep Learning (MIDL)},
-  year      = {2025},
-  organization = {MIDL},
-}
-```