{"id":31099035,"url":"https://github.com/hilab-git/tegda","last_synced_at":"2025-10-11T06:36:51.359Z","repository":{"id":299984814,"uuid":"1004127267","full_name":"HiLab-git/TEGDA","owner":"HiLab-git","description":null,"archived":false,"fork":false,"pushed_at":"2025-09-16T12:57:55.000Z","size":12682,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-09-16T14:55:42.626Z","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/HiLab-git.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,"zenodo":null}},"created_at":"2025-06-18T06:49:19.000Z","updated_at":"2025-09-16T12:57:58.000Z","dependencies_parsed_at":"2025-06-19T09:49:19.610Z","dependency_job_id":null,"html_url":"https://github.com/HiLab-git/TEGDA","commit_stats":null,"previous_names":["sherlockzyb/tegda"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/HiLab-git/TEGDA","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HiLab-git%2FTEGDA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HiLab-git%2FTEGDA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HiLab-git%2FTEGDA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HiLab-git%2FTEGDA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/HiLab-git","download_url":"https://codeload.github.com/HiLab-git/TEGDA/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HiLab-git%2FTEGDA/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279006452,"owners_count":26084107,"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","status":"online","status_checked_at":"2025-10-11T02:00:06.511Z","response_time":55,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":"2025-09-16T21:29:15.894Z","updated_at":"2025-10-11T06:36:51.336Z","avatar_url":"https://github.com/HiLab-git.png","language":"Python","readme":"# TEGDA: Test-time Evaluation-Guided Dynamic Adaptation for Medical Image Segmentation\n---\nThis is the official code for TEGDA: Test-time Evaluation-Guided Dynamic Adaptation for Medical Image Segmentation.\n\n[2025-06] Our work have been accepted by MICCAI 2025.\n\n\n## Overall Framework\n![](pictures/pipeline.png)\n\nOur contributions are summarized as follows:\n- We present a novel prediction quality evaluation metric based on **Agreement with Dropout Inferences calibrated by Confidence (ADIC)**, where the Dice score between predictions by the model and its dropout version is leveraged to assess the robustness of the model on a testing sample, then it is further calibrated by the confidence to become highly relevant to the real Dice value between the prediction and its ground-truth\n- We propose **Adaptive Feature Fusion-based Refinement (AFFR)** that adaptively fuses the feature of a sample with those with high ADIC values based on their similarity, leading to robust refined pseudo-labels.\n- We introduce ADIC-guided **Self-adaptive Model Updating (SMU)** that consists of ADIC-aware pseudo-label loss weighting and ADIC-aware mean teacher to improve the stability of adaptation.\n\n## Dataset\nDownload the BraTS-GLI and BraTS-PED datasets from [BraTS 2023](https://www.synapse.org/#!Synapse:syn51156910/wiki/), M\u0026Ms datasets from [M\u0026Ms](http://www.ub.edu/mnms).\n\n## How to use\n### Source model training\nUse\n```\ncd code\npython train_fully_supervised_2D.py # For M\u0026Ms dataset\npython train_fully_supervised_3D.py # For BraTS dataset\n```\nto get the source model for two datasets.\n\n### Test-time adaptation\nUse\n```\n./run.sh\n```\nto get the test-time adaptation results on two datasets.","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhilab-git%2Ftegda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhilab-git%2Ftegda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhilab-git%2Ftegda/lists"}