---
language:
- en
license: cc-by-nc-nd-4.0
tags:
- cxr
- ecg
- echocardiogram
- probabilistic modelling
- multimodal
- medical
pipeline_tag: other
---

# ProbMED: A Probabilistic Framework for Medical Multimodal Binding (ICCV 2025)
Probabilistic Modality-Enhanced Diagnosis (ProbMED), a multi-modal Med-VLPM that employs probabilistic contrastive learning to model distributions over embeddings rather than fixed-point, deterministic estimates. ProbMED aligns four distinct modalities—chest X-rays, electrocardiograms, echocardiograms, and clinical text—into a unified probabilistic embedding space.

<p align="center">
  <img src="assets/github_highlevel.png" width="40%">
</p>


## Installation
Clone the GitHub repository and install dependencies, instructions are found in the repo:
```bash
git clone git@github.com:mcintoshML/probMED.git
cd probMED
pip install -r requirements.txt
```

## Full Code Release
The model weights and inference is available with this code base. 

**We plan to release the full training and evaluation codebase upon the clinical journal submission to facilitate reproducibility, please stay tuned!**

## License
This work is licensed under the **Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)**.

You may share this work for non-commercial purposes, with proper attribution, but you may not modify it or use it commercially.

[![Creative Commons License](https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png)](https://creativecommons.org/licenses/by-nc-nd/4.0/)

[View Full License Details](https://creativecommons.org/licenses/by-nc-nd/4.0/)

## Citation
If you use ProbMED in your research (ICCV 2025), please cite:
```
@article{gao2025probmed,
  title={ProbMed: A Probabilistic Framework for Medical Multimodal Binding},
  author={Gao, Yuan and Kim, Sangwook and You, Jianzhong and McIntosh, Chris},
  journal={arXiv preprint arXiv:2509.25711},
  year={2025}
}
```