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--- |
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license: mit |
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pipeline_tag: image-segmentation |
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tags: |
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- medical |
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- foundation-model |
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- sam3 |
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- segmentation |
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--- |
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# Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentation |
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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. |
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- **Paper:** [Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentation](https://huggingface.co/papers/2601.10880) |
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- **Project Page:** [https://chongcongjiang.github.io/MedicalSAM3/](https://chongcongjiang.github.io/MedicalSAM3/) |
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- **Repository:** [https://github.com/AIM-Research-Lab/Medical-SAM3](https://github.com/AIM-Research-Lab/Medical-SAM3) |
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## 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. |
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## Citation |
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```bibtex |
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@article{jiang2026medicalsam3, |
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title={Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentation}, |
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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}, |
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journal={arXiv preprint arXiv:2601.10880}, |
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year={2026}, |
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url={https://arxiv.org/abs/2601.10880} |
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} |
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``` |
