HI-ML open-source toolbox

HI-ML helps to simplify and streamline work on deep learning models for healthcare and life sciences, by providing tested components (data loaders, pre-processing), deep learning models, and cloud integration tools. It is created and used for machine learning (ML) research by the Biomedical Imaging team (opens in new tab) in Microsoft Health Futures (opens in new tab). It is released at no-cost under an MIT open-source license to make it widely available for the global healthcare machine learning community, who can leverage our work. Find out more here (opens in new tab).

If you have any problems, find issues in the code, or have a feature request, then please create an issue on GitHub (opens in new tab). We monitor these issues and will look to respond via GitHub.

Yes. HI-ML has been designed with usability and flexibility at its core, built on PyTorch and making extensive use of Microsoft Azure. It makes it easy to use take full advantage of Azure to provide GPUs for training, secure and scalable data storage. Azure Machine Learning is used for scaling clusters 0 to N compute nodes to train models on multiple GPUs. Our toolbox uses Azure Machine Learning to manage DevOps for ML (MLOps), including experiment traceability, experiment transparency model reproducibility, model management, model deployment, integration with Git and Continuous Integration (CI). In addition, the toolkit supports more advanced ML development features including cross-validation, hyperparameter tuning, building ensemble models, and comparing new and existing models. For more details, see Azure Machine Learning – ML as a service | Microsoft Azure (opens in new tab). HI-ML can be used without Microsoft Azure for debugging and local development.

We have released HI-ML at no-cost as open-source software on GitHub (opens in new tab) under an MIT license to make these machine learning developments and technical components available to the community. These tools are open-source research projects and not Microsoft products. They take advantage of Microsoft Azure to make it easier to develop and deploy medical imaging models. If you have any feature requests, or find issues in the code, please create an issue on GitHub (opens in new tab). The Health Futures Biomedical Imaging team monitors these issues and will look to respond via GitHub.

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA. This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

We have released HI-ML at no-cost as open-source software on GitHub (opens in new tab) under an MIT license to make these machine learning developments and technical components available to the community. These tools are open-source research projects and not Microsoft products. They take advantage of Microsoft Azure to make it easier to develop and deploy ML models. For more information about Microsoft products, see Azure for Healthcare—Healthcare Solutions | Microsoft Azure (opens in new tab)

There are many organisations around the world building on these open-source tools for research. Healthcare providers, life sciences companies, and partners may build on this toolbox to develop their own ML products and services using Microsoft Azure. Any use beyond research is subject to testing and regulatory approval as appropriate, such as FDA clearance, CE marking, or in-house exemption controls. We’re excited to see how people and organizations build on this to improve patient care and accelerate life sciences discovery. For more information about Microsoft products, see Azure for Healthcare—Healthcare Solutions | Microsoft Azure (opens in new tab).

Healthcare providers, life sciences companies, and partners may use this OSS toolbox to develop their own ML products and services. Any use beyond research is subject to testing and regulatory approval as appropriate, such as FDA clearance, CE marking, or in-house exemption controls. We’re excited to see how people and organizations build on this to improve patient care. For more information about Microsoft products, see Azure for Healthcare—Healthcare Solutions | Microsoft Azure (opens in new tab).