Update README.md

README.md

@@ -1,3 +1,35 @@
 ---
 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}
+}
+```