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A curated list of foundation models for vision and language tasks in medical imaging
| Date | Stars |
|---|---|
| 2026-07-31 | 301 |
| 2026-08-01 | 301 |
| 2026-08-06 | 301 |
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# <p align=center>:fire:`Awesome Foundational Models in Medical Imaging `:fire:</p>
[](https://github.com/sindresorhus/awesome)
[](https://opensource.org/licenses/MIT)
[](https://makeapullrequest.com)
🔥🔥 This is a collection of awesome articles about foundation models in medical imaging🔥🔥
Our survey paper on arXiv: [Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision](https://arxiv.org/abs/2310.18689) ❤️
## Citation
If you find our work useful in your research, please consider citing:
```bibtex
@article{azad2023foundational,
title={Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision},
author={Azad, Bobby and Azad, Reza and Eskandari, Sania and Bozorgpour, Afshin and Kazerouni, Amirhossein and Rekik, Islem and Merhof, Dorit},
journal={arXiv preprint arXiv:2310.18689},
year={2023}
}
```
## Overview
Foundation models, large-scale pre-trained deep learning models adaptable to various tasks, have gained interest across deep learning applications. In the medical imaging field, they enable contextual reasoning, generalization, and prompt-based task adjustments. This survey provides an overview of foundation models in medical imaging, covering fundamental concepts, taxonomy based on training strategies, application domains, imaging modalities, and more. It highlights practical use cases, applications, future directions, and challenges, including interpretability, data management, computational needs, and contextual comprehension.
<p align="center">
<img src="https://github.com/xmindflow/Awesome-Foundation-Models-in-Medical-Imaging/assets/61879630/7a5fa0c3-b92a-4951-92cc-746e6766aa00" alt="Image Description">
</p>
We strongly encourage authors of relevant works to make a pull request and add their paper's information.
## Contents
- [Survey Papers](#survey-papers)
- [Papers](#papers)
- [Textual Prompted Models](#textual-prompted-models)
- [Contrastive](#contrastive)
- [Conversational](#conversational)
- [Generative](#generative)
- [Hybrid](#hybrid)
- [Visual Prompted Models](#visual-prompted-models)
- [Adaptations](#adaptations)
- [Generalist](#generalist)
## Survey Papers
**Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision**🔥<br>
*Bobby Azad, Reza Azad, Sania Eskandari, Afshin Bozorgpour, Amirhossein Kazerouni, Islem Rekik, Dorit Merhof*<br>
[28th Oct., 2023] [arXiv, 2023]<br>
[[Paper](https://arxiv.org/abs/2310.18689)]<br>
## Papers
### Textual Prompted Models
#### Contrastive
**Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning**<br>
*Weijian Huang, Cheng Li, Hao Yang, Jiarun Liu, Shanshan Wang*<br>
[12th Sep., 2023] [arXiv, 2023]<br>
[[Paper](https://arxiv.org/pdf/2309.05904.pdf)]<br>
**A visual-language foundation model for pathology image analysis using medical Twitter**<br>
*Zhi Huang, Federico Bianchi, Mert Yuksekgonul, Thomas J. Montine, James Zou*<br>
[17th Aug., 2023] [Nature Medicine, 2023]<br>
[[Paper](https://www.nature.com/articles/s41591-023-02504-3)] [[GitHub](https://tinyurl.com/webplip)]<br>
**ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders**<br>
*Shawn Xu, Lin Yang, Christopher Kelly, Marcin Sieniek, Timo Kohlberger, Martin Ma, Wei-Hung Weng, Atilla Kiraly, Sahar Kazemzadeh, Zakkai Melamed, Jungyeon Park, Patricia Strachan, Yun Liu, Chuck Lau, Preeti Singh, Christina Chen, Mozziyar Etemadi, Sreenivasa Raju Kalidindi, Yossi Matias, Katherine Chou, Greg S. Corrado, Shravya Shetty, Daniel Tse, Shruthi Prabhakara, Daniel Golden,Excerpt of 17,388 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e12e350c9e942fbb, topic:medical, name:medical imaging, desc:medical imaging
matched fp:e12e350c9e942fbb, topic:foundation-models