{"id":21905249,"url":"https://github.com/mmasoud1/meshfl","last_synced_at":"2025-08-14T18:39:31.248Z","repository":{"id":256817519,"uuid":"852862462","full_name":"Mmasoud1/MeshFL","owner":"Mmasoud1","description":"MeshNet distributed learning by using the NVFlare framework","archived":false,"fork":false,"pushed_at":"2025-02-10T21:20:16.000Z","size":1323,"stargazers_count":5,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-16T00:42:30.531Z","etag":null,"topics":["distributed-learning","federated-learning","nvflare","semantic-segmentation"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Mmasoud1.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2024-09-05T14:58:50.000Z","updated_at":"2025-05-05T15:21:20.000Z","dependencies_parsed_at":"2024-11-20T10:56:10.602Z","dependency_job_id":"aaa97afe-d4c2-4f3e-8aa5-9c53fa3866b8","html_url":"https://github.com/Mmasoud1/MeshFL","commit_stats":null,"previous_names":["mmasoud1/meshdist_nvflare","mmasoud1/meshfl"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/Mmasoud1/MeshFL","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mmasoud1%2FMeshFL","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mmasoud1%2FMeshFL/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mmasoud1%2FMeshFL/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mmasoud1%2FMeshFL/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Mmasoud1","download_url":"https://codeload.github.com/Mmasoud1/MeshFL/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mmasoud1%2FMeshFL/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270466379,"owners_count":24588791,"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":"2025-08-14T02:00:10.309Z","response_time":75,"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":["distributed-learning","federated-learning","nvflare","semantic-segmentation"],"created_at":"2024-11-28T16:32:07.245Z","updated_at":"2025-08-14T18:39:31.225Z","avatar_url":"https://github.com/Mmasoud1.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MeshFL [![Version](https://img.shields.io/badge/Version-1.0.0-brightgreen)]() [![MIT-License ](https://img.shields.io/badge/license-MIT-green)](https://github.com/Mmasoud1/MeshFL/blob/main/LICENSE) [![PyTorch](https://img.shields.io/badge/PyTorch-Trained%20Model-blue)]()\n\n\u003cdiv align=\"center\"\u003e\n\n**[Updates](#Updates) \u0026emsp; [Doc](https://github.com/Mmasoud1/MeshFL/wiki/) \u0026emsp; [News!](#News)**\n\n\u003c/div\u003e\n\n\u003cbr\u003e\n \u003cimg src=\"https://github.com/Mmasoud1/MeshFL/blob/main/css/logo/MeshFL.png\"  width=\"25%\" align=\"right\"\u003e\n\n  \u003cp align=\"justify\"\u003e\n\u003cb\u003eMeshFL\u003c/b\u003e is an advanced framework for distributed learning in neuroimaging. Built on the \u003ca href=\"https://medium.com/pytorch/catalyst-neuro-a-3d-brain-segmentation-pipeline-for-mri-b1bb1109276a\" target=\"_blank\"  style=\"text-decoration: none\"\u003e MeshNet\u003c/a\u003e models and \u003ca href=\"https://developer.nvidia.com/flare\" target=\"_blank\"  style=\"text-decoration: none\"\u003e NVFlare\u003c/a\u003e, it enables federated training for 3D MRI brain segmentation across decentralized data sites, maintaining privacy and efficiency.\n \u003c/p\u003e\n\n\u003cp align=\"justify\"\u003e\n For more information about MeshFL, please refer to this detailed \u003cb\u003e\u003ca href=\"https://github.com/Mmasoud1/MeshFL/wiki/\"  style=\"text-decoration: none\"\u003eWiki\u003c/a\u003e\u003c/b\u003e\n\u003c/p\u003e\n\n\u003cbr\u003e\n\n## Key Features\n\n* Federated training of the MeshNet model for 3D MRI brain segmentation.\n* Supports decentralized learning across multiple sites using NVFlare.\n* Automated data handling and splitting.\n* Optimized GPU usage.\n* Customizable training workflows with integrated Dice score evaluation.\n\n\u003cbr\u003e\n\u003cdiv align=\"center\"\u003e\n\n![Interface](https://github.com/Mmasoud1/MeshFL/blob/main/css/images/MeshFL_animated_output.gif)\n\n**MeshFL training and MRI segmentation outputs**\n\u003c/div\u003e\n\n## Getting Started\nTo start MeshFL, please refer to this steps \u003cb\u003e\u003ca href=\"https://github.com/Mmasoud1/MeshFL/wiki/Setup\"  style=\"text-decoration: none\"\u003ehere\u003c/a\u003e\u003c/b\u003e\n\n\n## Updates\n\n* MeshFL \u003ca href= \"https://github.com/Mmasoud1/MeshFL/releases/tag/v1.0.0\" target=\"_blank\"  style=\"text-decoration: none\"\u003e v1.0.0 \u003c/a\u003e has been released\n\n## News!\n\n## Contributions and Authorship Guidelines\n\nWe welcome contributions to MeshFL! Whether it's bug fixes, new features, or documentation improvements, feel free to submit an issue or a pull request.\n\nIf you modify or extend MeshFL in a derivative work intended for publication (such as a research paper or software tool), please cite and acknowledge the original MeshFL project and the original authors. \n\nWe also request that significant contributions to derivative works be recognized by including original authors as co-authors, where appropriate.\n\n\n## Acknowledgments\n\n[NVFlare:](https://developer.nvidia.com/flare) Federated learning framework. \n\n[MeshNet:](https://medium.com/pytorch/catalyst-neuro-a-3d-brain-segmentation-pipeline-for-mri-b1bb1109276a) Volumetric dilated convolutional neural network architecture for MRI segmentation.\n\n## Funding\n\nMeshFL release V1.0.0 was funded by the NIH grant R01DA040487.    \n\n\u003cbr /\u003e\n\n\u003cdiv align=\"center\"\u003e\n\n\u003cimg src='https://github.com/Mmasoud1/MeshFL/blob/main/css/logo/TReNDS_logo.jpg' width='300' height='100'\u003e\u003c/img\u003e\n\n\u003c/div\u003e\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmmasoud1%2Fmeshfl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmmasoud1%2Fmeshfl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmmasoud1%2Fmeshfl/lists"}