| | --- |
| | tags: |
| | - model_hub_mixin |
| | - pytorch_model_hub_mixin |
| | license: mit |
| | --- |
| | |
| | ## 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) |
| | - Tutorial: [Notebook](https://colab.research.google.com/drive/1_8Ysd8mCKuLX_Q86e-7pOAHFbSR9F4aZ?usp=sharing) |
| |
|
| | ### 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} |
| | } |
| | ``` |
