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---
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language:
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- en
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license: cc-by-nc-nd-4.0
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tags:
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- cxr
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- ecg
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- echocardiogram
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- probabilistic modelling
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- multimodal
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- medical
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pipeline_tag: other
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---
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# ProbMED: A Probabilistic Framework for Medical Multimodal Binding (ICCV 2025)
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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.
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<p align="center">
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<img src="assets/github_highlevel.png" width="40%">
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</p>
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## Installation
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Clone the GitHub repository and install dependencies, instructions are found in the repo:
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```bash
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git clone [email protected]:mcintoshML/probMED.git
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cd probMED
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pip install -r requirements.txt
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```
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## Full Code Release
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The model weights and inference is available with this code base.
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**We plan to release the full training and evaluation codebase upon the clinical journal submission to facilitate reproducibility, please stay tuned!**
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## License
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This work is licensed under the **Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)**.
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You may share this work for non-commercial purposes, with proper attribution, but you may not modify it or use it commercially.
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[](https://creativecommons.org/licenses/by-nc-nd/4.0/)
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[View Full License Details](https://creativecommons.org/licenses/by-nc-nd/4.0/)
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## Citation
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If you use ProbMED in your research (ICCV 2025), please cite:
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```
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@article{gao2025probmed,
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title={ProbMed: A Probabilistic Framework for Medical Multimodal Binding},
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author={Gao, Yuan and Kim, Sangwook and You, Jianzhong and McIntosh, Chris},
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journal={arXiv preprint arXiv:2509.25711},
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year={2025}
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}
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``` |
