Medical SAM3
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...
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 17,7 MB–9,59 GB
- Versión
- vRevision 716c9e1
- Actualizado
- 1 nov 2026
- Descargas
- 5
- Fuente
- Hugging Face
Descripción de la fuente
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
- Project Page: https://chongcongjiang.github.io/MedicalSAM3/
- Repository: 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
@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}
}
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Extractos de fuentes
3 extractos--- 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} } ```
Source context: 5 downloads · 15 likes · Pipeline image-segmentation · Repo ChongCong/Medical-SAM3
Source context: 4 downloads · 12 likes · Pipeline image-segmentation · Repo ChongCong/Medical-SAM3
