File size: 1,874 Bytes
c9c8fe1 f2c50d7 b15111d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 |
---
license: mit
pipeline_tag: image-segmentation
tags:
- medical
- foundation-model
- sam3
- segmentation
---
# Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentation
Medical SAM3 is a foundation model for universal prompt-driven medical image segmentation, obtained by fully fine-tuning SAM3 on large-scale, heterogeneous 2D and 3D medical imaging datasets with paired segmentation masks and text prompts.
- **Paper:** [Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentation](https://huggingface.co/papers/2601.10880)
- **Project Page:** [https://chongcongjiang.github.io/MedicalSAM3/](https://chongcongjiang.github.io/MedicalSAM3/)
- **Repository:** [https://github.com/AIM-Research-Lab/Medical-SAM3](https://github.com/AIM-Research-Lab/Medical-SAM3)
## Introduction
Promptable segmentation foundation models such as SAM3 have demonstrated strong generalization capabilities through interactive and concept-based prompting. However, their direct applicability to medical image segmentation remains limited by severe domain shifts.
By fine-tuning SAM3's model parameters on 33 datasets spanning 10 medical imaging modalities, Medical SAM3 acquires robust domain-specific representations while preserving prompt-driven flexibility. Experiments across organs, imaging modalities, and dimensionalities demonstrate consistent and significant performance gains.
## Citation
```bibtex
@article{jiang2026medicalsam3,
title={Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentation},
author={Jiang, Chongcong and Ding, Tianxingjian and Song, Chuhan and Tu, Jiachen and Yan, Ziyang and Shao, Yihua and Wang, Zhenyi and Shang, Yuzhang and Han, Tianyu and Tian, Yu},
journal={arXiv preprint arXiv:2601.10880},
year={2026},
url={https://arxiv.org/abs/2601.10880}
}
``` |