Complete model card

README.md

@@ -1,9 +1,49 @@
 ---
 license: cc-by-nc-sa-4.0
-arxiv: 2509.06831
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 # Leveraging generic foundation models for multimodal surgical data analysis
 
 Model repository for the paper **Leveraging generic foundation models for multimodal surgical data analysis** by Simon
-Pezold, Jérôme A. Kurylec, Jan S. Liechti, Beat P. Müller, and Joël L. Lavanchy.
+Pezold, Jérôme A. Kurylec, Jan S. Liechti, Beat P. Müller, and Joël L. Lavanchy. For more details, see the [paper](https://arxiv.org/abs/2509.06831) and its
+[code repository](https://github.com/DigitalSurgeryLab-Basel/ML-CDS-2025).
+
+## Model weights and usage
+
+* **Finetuned V-JEPA** (ours): We finetuned *V-JEPA ViT-L* on unlabeled endoscopic surgical videos, as described in the paper.
+  The resulting model weights can be downloaded [here](./jepa-12h.pth.tar).
+
+* **Pretrained V-JEPA** (third-party): The model weights that formed the basis for finetuning can be downloaded via the links provided in Meta Research's
+  [V-JEPA v1 code repository](https://github.com/facebookresearch/jepa): use the link under *model zoo / pretrained models /
+  ViT-L / checkpoint* or [this direct link](https://dl.fbaipublicfiles.com/jepa/vitl16/vitl16.pth.tar). Please note that we are not
+  affiliated with Meta Research in any way.
+
+For details on how to use the provided model weights with the approach proposed in the paper, follow the instructions in its
+[code repository](https://github.com/DigitalSurgeryLab-Basel/ML-CDS-2025).
+
+## Data, distribution, and licensing
+
+As we utilized public datasets for finetuning, we distribute our model weights under the same license:
+[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
+For more details, please refer to the license file linked above.
+
+Videos from the following datasets were used for finetuning:
+
+* [Heidelberg colorectal (HeiCo)](https://www.synapse.org/Synapse:syn21903917/files/)
+* [Cholec80](http://camma.u-strasbg.fr/datasets/)
+* [MultiBypass140](https://github.com/CAMMA-public/MultiBypass140)
+
+We are grateful to all authors for creating and publicly releasing these datasets!
+
+## Citing
+
+If you find our work useful, please consider citing:
+```bibtex
+@article{pezold2025leveraging,
+    title = {Leveraging Generic Foundation Models for Multimodal Surgical Data Analysis}, 
+    author = {Pezold, Simon and Kurylec, Jérôme A. and Liechti, Jan S. and Müller, Beat P. and Lavanchy, Joël L.},
+    journal = {arXiv preprint},
+    year = {2025},
+    doi = {10.48550/arXiv.2509.06831},
+}
+```