{"id":13510731,"url":"https://github.com/microsoft/InnerEye-DeepLearning","last_synced_at":"2025-03-30T16:35:02.150Z","repository":{"id":37078806,"uuid":"259878805","full_name":"microsoft/InnerEye-DeepLearning","owner":"microsoft","description":"Medical Imaging Deep Learning library to train and deploy 3D segmentation models on Azure Machine Learning","archived":true,"fork":false,"pushed_at":"2024-03-21T09:43:17.000Z","size":8965,"stargazers_count":549,"open_issues_count":89,"forks_count":142,"subscribers_count":32,"default_branch":"main","last_synced_at":"2024-08-02T02:17:13.402Z","etag":null,"topics":["azure","deep-learning","healthcare","medical-imaging"],"latest_commit_sha":null,"homepage":"https://aka.ms/innereyeoss","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/microsoft.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":"SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2020-04-29T09:15:05.000Z","updated_at":"2024-07-27T23:36:14.000Z","dependencies_parsed_at":"2024-03-21T11:06:42.563Z","dependency_job_id":null,"html_url":"https://github.com/microsoft/InnerEye-DeepLearning","commit_stats":null,"previous_names":[],"tags_count":8,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/microsoft%2FInnerEye-DeepLearning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/microsoft%2FInnerEye-DeepLearning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/microsoft%2FInnerEye-DeepLearning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/microsoft%2FInnerEye-DeepLearning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/microsoft","download_url":"https://codeload.github.com/microsoft/InnerEye-DeepLearning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":222566739,"owners_count":17004237,"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":["azure","deep-learning","healthcare","medical-imaging"],"created_at":"2024-08-01T02:01:52.161Z","updated_at":"2025-03-30T16:35:02.144Z","avatar_url":"https://github.com/microsoft.png","language":"Python","readme":"# This project is now archived\n\nThis project is no longer under active maintenance. It is read-only, but you can still clone or fork the repo. [Check here for further info](https://docs.github.com/en/repositories/archiving-a-github-repository/archiving-repositories).\nPlease contact innereye_info@service.microsoft.com if you run into trouble with the \"Archived\" state of the repo.\n\n# InnerEye-DeepLearning\n\n[![Build Status](https://innereye.visualstudio.com/InnerEye/_apis/build/status/InnerEye-DeepLearning/InnerEye-DeepLearning-PR?branchName=main)](https://innereye.visualstudio.com/InnerEye/_build?definitionId=112\u0026branchName=main)\n\nInnerEye-DeepLearning (IE-DL) is a toolbox for easily training deep learning models on 3D medical images. Simple to run both locally and in the cloud with [AzureML](https://docs.microsoft.com/en-gb/azure/machine-learning/), it allows users to train and run inference on the following:\n\n- Segmentation models.\n- Classification and regression models.\n- Any PyTorch Lightning model, via a [bring-your-own-model setup](docs/source/md/bring_your_own_model.md).\n\nIn addition, this toolbox supports:\n\n- Cross-validation using AzureML, where the models for individual folds are trained in parallel. This is particularly important for the long-running training jobs often seen with medical images.\n- Hyperparameter tuning using [Hyperdrive](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters).\n- Building ensemble models.\n- Easy creation of new models via a configuration-based approach, and inheritance from an existing architecture.\n\n## Documentation\n\nFor all documentation, including setup guides and APIs, please refer to the [IE-DL Read the Docs site](https://innereye-deeplearning.readthedocs.io/#).\n\n## Quick Setup\n\nThis quick setup assumes you are using a machine running Ubuntu with Git, Git LFS, Conda and Python 3.7+ installed. Please refer to the [setup guide](docs/source/md/environment.md) for more detailed instructions on getting InnerEye set up with other operating systems and installing the above prerequisites.\n\n1. Clone the InnerEye-DeepLearning repo by running the following command:\n\n   ```shell\n   git clone --recursive https://github.com/microsoft/InnerEye-DeepLearning \u0026\u0026 cd InnerEye-DeepLearning\n   ```\n\n2. Create and activate your conda environment:\n\n   ```shell\n   conda env create --file environment.yml \u0026\u0026 conda activate InnerEye\n   ```\n\n3. Verify that your installation was successful by running the HelloWorld model (no GPU required):\n\n   ```shell\n   python InnerEye/ML/runner.py --model=HelloWorld\n   ```\n\nIf the above runs with no errors: Congratulations! You have successfully built your first model using the InnerEye toolbox.\n\nIf it fails, please check the\n[troubleshooting page on the Wiki](https://github.com/microsoft/InnerEye-DeepLearning/wiki/Issues-with-code-setup-and-the-HelloWorld-model).\n\n## Full InnerEye Deployment\n\nWe offer a companion set of open-sourced tools that help to integrate trained CT segmentation models with clinical\nsoftware systems:\n\n- The [InnerEye-Gateway](https://github.com/microsoft/InnerEye-Gateway) is a Windows service running in a DICOM network,\nthat can route anonymized DICOM images to an inference service.\n- The [InnerEye-Inference](https://github.com/microsoft/InnerEye-Inference) component offers a REST API that integrates\nwith the InnerEye-Gateway, to run inference on InnerEye-DeepLearning models.\n\nDetails can be found [here](docs/source/md/deploy_on_aml.md).\n\n![docs/deployment.png](docs/source/images/deployment.png)\n\n## Benefits of InnerEye-DeepLearning\n\nIn combiniation with the power of AzureML, InnerEye provides the following benefits:\n\n- **Traceability**: AzureML keeps a full record of all experiments that were executed, including a snapshot of the code. Tags are added to the experiments automatically, that can later help filter and find old experiments.\n- **Transparency**: All team members have access to each other's experiments and results.\n- **Reproducibility**: Two model training runs using the same code and data will result in exactly the same metrics. All sources of randomness are controlled for.\n- **Cost reduction**: Using AzureML, all compute resources (virtual machines, VMs) are requested at the time of starting the training job and freed up at the end. Idle VMs will not incur costs. Azure low priority nodes can be used to further reduce costs (up to 80% cheaper).\n- **Scalability**: Large numbers of VMs can be requested easily to cope with a burst in jobs.\n\nDespite the cloud focus, InnerEye is designed to be able to run locally too, which is important for model prototyping, debugging, and in cases where the cloud can't be used. Therefore, if you already have GPU machines available, you will be able to utilize them with the InnerEye toolbox.\n\n## Licensing\n\n[MIT License](/LICENSE)\n\n**You are responsible for the performance, the necessary testing, and if needed any regulatory clearance for\n any of the models produced by this toolbox.**\n\n## Acknowledging usage of Project InnerEye OSS tools\n\nWhen using Project InnerEye open-source software (OSS) tools, please acknowledge with the following wording:\n\n\u003e This project used Microsoft Research's Project InnerEye open-source software tools ([https://aka.ms/InnerEyeOSS](https://aka.ms/InnerEyeOSS)).\n\n## Contact\n\nIf you have any feature requests, or find issues in the code, please create an\n[issue on GitHub](https://github.com/microsoft/InnerEye-DeepLearning/issues).\n\nPlease send an email to InnerEyeInfo@microsoft.com if you would like further information about this project.\n\n## Publications\n\nOktay O., Nanavati J., Schwaighofer A., Carter D., Bristow M., Tanno R., Jena R., Barnett G., Noble D., Rimmer Y., Glocker B., O’Hara K., Bishop C., Alvarez-Valle J., Nori A.: Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers. JAMA Netw Open. 2020;3(11):e2027426. [doi:10.1001/jamanetworkopen.2020.27426](https://pubmed.ncbi.nlm.nih.gov/33252691/)\n\nBannur S., Oktay O., Bernhardt M, Schwaighofer A., Jena R., Nushi B., Wadhwani S., Nori A., Natarajan K., Ashraf S., Alvarez-Valle J., Castro D. C.: Hierarchical Analysis of Visual COVID-19 Features from Chest Radiographs. ICML 2021 Workshop on Interpretable Machine Learning in Healthcare. [https://arxiv.org/abs/2107.06618](https://arxiv.org/abs/2107.06618)\n\nBernhardt M., Castro D. C., Tanno R., Schwaighofer A., Tezcan K. C., Monteiro M., Bannur S., Lungren M., Nori S., Glocker B., Alvarez-Valle J., Oktay. O: Active label cleaning for improved dataset quality under resource constraints. [https://www.nature.com/articles/s41467-022-28818-3](https://www.nature.com/articles/s41467-022-28818-3). Accompanying code [InnerEye-DataQuality](https://github.com/microsoft/InnerEye-DeepLearning/blob/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality/README.md)\n\n## Contributing\n\nThis project welcomes contributions and suggestions.  Most contributions require you to agree to a\nContributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us\nthe rights to use your contribution. For details, visit [https://cla.opensource.microsoft.com](https://cla.opensource.microsoft.com).\n\nWhen you submit a pull request, a CLA bot will automatically determine whether you need to provide\na CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions\nprovided by the bot. You will only need to do this once across all repos using our CLA.\n\nThis project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).\nFor more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or\ncontact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.\n\n## Maintenance\n\nThis toolbox is maintained by the [Microsoft Medical Image Analysis team](https://www.microsoft.com/en-us/research/project/medical-image-analysis/).\n","funding_links":[],"categories":["Python","其他_生物医药","azure"],"sub_categories":["网络服务_其他"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmicrosoft%2FInnerEye-DeepLearning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmicrosoft%2FInnerEye-DeepLearning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmicrosoft%2FInnerEye-DeepLearning/lists"}