{"id":22067852,"url":"https://github.com/xmed-lab/TP-Mamba","last_synced_at":"2025-07-24T05:30:53.915Z","repository":{"id":259207496,"uuid":"823681909","full_name":"xmed-lab/TP-Mamba","owner":"xmed-lab","description":null,"archived":false,"fork":false,"pushed_at":"2024-10-24T15:26:10.000Z","size":12,"stargazers_count":3,"open_issues_count":2,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-10-25T20:24:37.465Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/xmed-lab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-07-03T13:57:20.000Z","updated_at":"2024-10-24T15:26:13.000Z","dependencies_parsed_at":"2024-10-27T08:31:52.995Z","dependency_job_id":null,"html_url":"https://github.com/xmed-lab/TP-Mamba","commit_stats":null,"previous_names":["xmed-lab/tp-mamba"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xmed-lab%2FTP-Mamba","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xmed-lab%2FTP-Mamba/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xmed-lab%2FTP-Mamba/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xmed-lab%2FTP-Mamba/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/xmed-lab","download_url":"https://codeload.github.com/xmed-lab/TP-Mamba/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":227421173,"owners_count":17775001,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-30T20:03:33.900Z","updated_at":"2024-11-30T20:04:26.654Z","avatar_url":"https://github.com/xmed-lab.png","language":"Python","funding_links":[],"categories":["Paper List"],"sub_categories":["Follow-up Papers"],"readme":"\u003cdiv align=center\u003e\n\u003ch1 style=\"font-family: 'Cursive', 'Comic Sans MS', sans-serif;\"\u003e\nEfficiently Adapting Vision Foundational Models on 3D Medical Image Segmentation 🚀\n\u003c/h1\u003e\n\u003c/div\u003e\n   \n\u003ca href=\"https://xmengli.github.io/\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/%F0%9F%9A%80-XMed_LAB-ed6c00.svg?style=flag\"\u003e\n\u003c/a\u003e\n\u003ca href='https://papers.miccai.org/miccai-2024/paper/2184_paper.pdf'\u003e\n    \u003cimg src='https://img.shields.io/badge/miccai24-@TP_Mamba-red'\u003e\n\u003c/a\u003e\n\nOfficial PyTorch implementation for our works on the topic of **efficiently adapting the pre-trained Vision Foundational Models (VFM) on 3D Medical Image Segmentation task**.\n\n[1] [\"Tri-Plane Mamba: Efficiently Adapting Segment Anything Model for 3D Medical Images\"](https://papers.miccai.org/miccai-2024/paper/2184_paper.pdf) ([MICCAI 2024](https://papers.miccai.org/miccai-2024))\n\n## 🌊🌊🌊 News\n\n💧 ***[2024-10-22]*** Re-organize and Upload partial core codes.\n\n## 🔥🔥🔥 Contributions\nWe 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.\n\n🔥 ***Data-Efficient***: Use less data to achieve more competitive performance, such as semi-supervised, few-shot, zero-shot, and so on.\n\n🔥 ***Parameter-Efficient***: Enhance the representation by lightweight adapters, such as local-feature, global-feature, or other existing adapters. \n\n## 🧰 Installation\n🔨 TODO \n\n## ⭐⭐⭐ Usage\n💡 Supported Adapters\n| Name   | Type   | Supported   |\n|------------|------------|------------|\n| Baseline (Frozen SAM) | None | ✔️|\n| LoRA | pixel-independent | ✔️|\n| SSF | pixel-independent | TODO |\n| multi-scale conv| local | ✔️|\n| PPM| local | TODO |\n| Mamba| global | TODO |\n| Linear Attention| global | TODO |\n\n\n## 📋 Results and Models\n📌 TODO \n\n## 📚 Citation\n\nIf you think our paper helps you, please feel free to cite it in your publications.\n\n📗 TP-Mamba\n```bash\n@InProceedings{Wan_TriPlane_MICCAI2024,\n        author = { Wang, Hualiang and Lin, Yiqun and Ding, Xinpeng and Li, Xiaomeng},\n        title = { { Tri-Plane Mamba: Efficiently Adapting Segment Anything Model for 3D Medical Images } },\n        booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},\n        year = {2024},\n        publisher = {Springer Nature Switzerland},\n        volume = {LNCS 15009},\n        month = {October},\n        page = {pending}\n}\n```\n\n\n## 🍻 Acknowledge\nWe sincerely appreciate these precious repositories 🍺[MONAI](https://github.com/Project-MONAI/MONAI) and 🍺[SAM](https://github.com/facebookresearch/segment-anything).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fxmed-lab%2FTP-Mamba","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fxmed-lab%2FTP-Mamba","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fxmed-lab%2FTP-Mamba/lists"}