{"id":29085174,"url":"https://github.com/imoonlab/dhgformer","last_synced_at":"2026-03-14T16:34:45.911Z","repository":{"id":300881292,"uuid":"1005490651","full_name":"iMoonLab/DHGFormer","owner":"iMoonLab","description":"DHGFormer: Dynamic Hierarchical Graph Transformer for Disorder Brain Disease Diagnosis","archived":false,"fork":false,"pushed_at":"2025-06-24T03:56:19.000Z","size":4491,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-06-24T04:38:07.389Z","etag":null,"topics":["brain","graph-transformer"],"latest_commit_sha":null,"homepage":"","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/iMoonLab.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":"2025-06-20T10:02:07.000Z","updated_at":"2025-06-24T03:56:22.000Z","dependencies_parsed_at":"2025-06-24T04:48:17.002Z","dependency_job_id":null,"html_url":"https://github.com/iMoonLab/DHGFormer","commit_stats":null,"previous_names":["imoonlab/dhgformer"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/iMoonLab/DHGFormer","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iMoonLab%2FDHGFormer","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iMoonLab%2FDHGFormer/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iMoonLab%2FDHGFormer/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iMoonLab%2FDHGFormer/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/iMoonLab","download_url":"https://codeload.github.com/iMoonLab/DHGFormer/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iMoonLab%2FDHGFormer/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":262343579,"owners_count":23296354,"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":["brain","graph-transformer"],"created_at":"2025-06-27T22:30:40.492Z","updated_at":"2026-03-14T16:34:45.901Z","avatar_url":"https://github.com/iMoonLab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv align=\"center\"\u003e\n\u003ch2\u003eDHGFormer: Dynamic Hierarchical Graph Transformer for Disorder Brain Disease Diagnosis\u003c/h2\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cb\u003eRundong Xue, Hao Hu, Zeyu Zhang, Xiangmin Han\u003csup\u003e*\u003c/sup\u003e, Juan Wang, Yue Gao, Shaoyi Du\u003csup\u003e*\u003c/sup\u003e\u003c/b\u003e\n\u003c/p\u003e\n\nAccepted by _**MICCAI 2025**_\n\n[[Paper]](https://papers.miccai.org/miccai-2025/paper/1576_paper.pdf)\n\n\u003c/div\u003e\n\n## Overview\n\u003cdiv align=\"center\"\u003e\n    \u003cimg src=\"figures/pipeline.png\"\u003e\n\u003c/div\u003e\n\n\n**Figure 1. The framework of the proposed DHGFormer.**\n\n\n**_Abstract -_** The functional brain network exhibits a hierarchical characterized organization, balancing localized specialization with global integration through multi-scale hierarchical connectivity. While graph-based methods have advanced brain network analysis, conventional graph neural networks (GNNs) face interpretational limitations when modeling functional connectivity (FC) that encodes excitatory/inhibitory distinctions, often resorting to oversimplified edge weight transformations. Existing methods usually inadequately represent the brain's hierarchical organization, potentially missing critical information about multi-scale feature interactions. To address these limitations, we propose a novel brain network generation and analysis approach--Dynamic Hierarchical Graph Transformer (DHGFormer). Specifically, our method introduces an FC-inspired dynamic attention mechanism that adaptively encodes brain excitatory/inhibitory connectivity patterns into transformer-based representations, enabling dynamic adjustment of the functional brain network. Furthermore, we design hierarchical GNNs that consider prior functional subnetwork knowledge to capture intra-subnetwork homogeneity and inter-subnetwork heterogeneity, thereby enhancing GNN performance in brain disease diagnosis tasks. Extensive experiments on the ABIDE and ADNI datasets demonstrate that DHGFormer consistently outperforms state-of-the-art methods in diagnosing neurological disorders.\n\n## Get Started\n### 1. Data Preparation\nDownload the ABIDE dataset from [here](https://drive.google.com/file/d/14UGsikYH_SQ-d_GvY2Um2oEHw3WNxDY3/view?usp=sharing).\n\n### 2. Usage\nRun the following command to train the model.\n```bash\npython main.py --config_filename setting/abide_DHGFormer.yaml\n```\n\n## Cite our work\n```bibtex\n@inproceedings{xue2025dhgformer,\n  title={DHGFormer: Dynamic Hierarchical Graph Transformer for Disorder Brain Disease Diagnosis},\n  author={Xue, Rundong and Hu, Hao and Zhang, Zeyu and Han, Xiangmin and Wang, Juan and Gao, Yue and Du, Shaoyi},\n  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},\n  pages={268--278},\n  year={2025},\n  organization={Springer}\n}\n```\n\n## License\nThe source code is free for research and education use only. Any comercial use should get formal permission first.\n\nThis repo benefits from [FBNETGEN](https://github.com/Wayfear/FBNETGEN).  Thanks for their wonderful works.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fimoonlab%2Fdhgformer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fimoonlab%2Fdhgformer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fimoonlab%2Fdhgformer/lists"}