- [2026-01-20]: 🚀 Pretrained weights for Medical-SAM3 are released!
- [2026-01-15]: 📄 Paper is available on arXiv.
We provide a comprehensive toolkit to run inference on diverse medical datasets (e.g., CHASE_DB1, Synapse, etc.).
The inference pipeline supports:
- 📊 Model Evaluation: Run Medical-SAM3 on supported datasets with a single command.
- ⚖️ Baseline Comparison: Compare performance against the vanilla SAM3 or other baselines.
- 🖼️ Visualization: Generate and save segmentation masks for qualitative analysis.
| Feature | Status | Description |
|---|---|---|
| Demo | 🚧 Doing | Online interactive demo. |
| Data Scaling | 🚧 Doing | Significantly expand the training corpus and evaluate on broader and more diverse medical datasets. |
| Training Code | 📅 Planned | Release full training scripts and data construction guidelines. |
| Medical-SAM3 Agent | 📅 Planned | Integrate LLMs to enable agentic reasoning and interaction for segmentation tasks. |
📢 We are actively updating this repository. If you are interested in any features above, feel free to open an issue!
If you find Medical-SAM3 useful for your research or work, please consider citing our paper:
@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}
}