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<div align=center> <h1 style="font-family: 'Cursive', 'Comic Sans MS', sans-serif;"> Efficiently Adapting Vision Foundational Models on 3D Medical Image Segmentation 🚀 </h1> </div> <a href="https://xmengli.github.io/"> <img src="https://img.shields.io/badge/%F0%9F%9A%80-XMed_LAB-ed6c00.svg?style=flag"> </a> <a href='https://papers.miccai.org/miccai-2024/paper/2184_paper.pdf'> <img src='https://img.shields.io/badge/miccai24-@TP_Mamba-red'> </a>Official PyTorch implementation for our works on the topic of efficiently adapting the pre-trained Vision Foundational Models (VFM) on 3D Medical Image Segmentation task.
[1] "Tri-Plane Mamba: Efficiently Adapting Segment Anything Model for 3D Medical Images" (MICCAI 2024)
🌊🌊🌊 News
💧 [2024-10-22] Re-organize and Upload partial core codes.
🔥🔥🔥 Contributions
We foucs on proposing more advanced adapters or training algorithms to adapt the pre-trained VFM (both natural and medical-specific models) on 3d medical image segmentation.
🔥 Data-Efficient: Use less data to achieve more competitive performance, such as semi-supervised, few-shot, zero-shot, and so on.
🔥 Parameter-Efficient: Enhance the representation by lightweight adapters, such as local-feature, global-feature, or other existing adapters.
🧰 Installation
🔨 TODO
⭐⭐⭐ Usage
💡 Supported Adapters
Name | Type | Supported |
---|---|---|
Baseline (Frozen SAM) | None | ✔️ |
LoRA | pixel-independent | ✔️ |
SSF | pixel-independent | TODO |
multi-scale conv | local | ✔️ |
PPM | local | TODO |
Mamba | global | TODO |
Linear Attention | global | TODO |
📋 Results and Models
📌 TODO
📚 Citation
If you think our paper helps you, please feel free to cite it in your publications.
📗 TP-Mamba
@InProceedings{Wan_TriPlane_MICCAI2024,
author = { Wang, Hualiang and Lin, Yiqun and Ding, Xinpeng and Li, Xiaomeng},
title = { { Tri-Plane Mamba: Efficiently Adapting Segment Anything Model for 3D Medical Images } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
year = {2024},
publisher = {Springer Nature Switzerland},
volume = {LNCS 15009},
month = {October},
page = {pending}
}
🍻 Acknowledge
We sincerely appreciate these precious repositories 🍺MONAI and 🍺SAM.