| --- |
| 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 |
|
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| 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. |
|
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| 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} |
| } |
| ``` |
