Update README.md

README.md

@@ -2,9 +2,34 @@
 tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
+license: mit
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: [More Information Needed]
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+## A Reality Check of Vision-Language Pre-training in Radiology: Have We Progressed Using Text?
+
+- Code: [DLILP](https://github.com/jusiro/DLILP)
+- Paper: [IPMI 2025](https://link.springer.com/chapter/10.1007/978-3-031-96625-5_20) - [ArXiv](https://arxiv.org/abs/2504.05227)
+- Docs: [Documentation](https://github.com/jusiro/DLILP)
+
+### About "CONVIRT" weights:
+
+- Pre-trained using a vanilla CLIP contrastive loss - a very similar pre-training as earlier proposed in [CONVIRT](https://arxiv.org/abs/2010.00747) paper (2020).
+- Pre-trained on MIMIC.
+
+If you find this repository useful, please consider citing this paper:
+```
+@inproceedings{convirt,
+  author = {Yuhao Zhang and others},
+  booktitle = {MHLC},
+  pages = {1-24},
+  title = {Contrastive Learning of Medical Visual Representations from Paired Images and Text},
+  year = {2022},
+}
+
+@inproceedings{dlilp,
+    title={A Reality Check of Vision-Language Pre-training in Radiology: Have We Progressed Using Text?},
+    author={Julio Silva-Rodríguez and Jose Dolz and Ismail {Ben Ayed}},
+    booktitle={Information Processing in Medical Imaging (IPMI)},
+    year={2025}
+}
+```