{"id":855669,"date":"2022-07-14T06:08:46","date_gmt":"2022-07-14T13:08:46","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&p=855669"},"modified":"2024-04-26T13:53:20","modified_gmt":"2024-04-26T20:53:20","slug":"hi-ml-oss-toolbox","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/hi-ml-oss-toolbox\/","title":{"rendered":"HI-ML OSS toolbox"},"content":{"rendered":"
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\n\t\t\t\"Health\t\t<\/div>\n\t\t\n\t\t
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HI-ML open-source toolbox<\/h1>\n\n\n\n

Open-source tools to help simplify deep learning models for healthcare and life sciences<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n

The Health Intelligence Machine Learning (HI-ML) OSS toolbox 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 multiple groups<\/a> in Microsoft Health Futures<\/a>. It is released at no-cost under an MIT open-source license and supports the FAIR Principles for open science (opens in new tab)<\/span><\/a> to make it widely available for the global healthcare machine learning community, who can leverage our work.<\/p>\n\n\n\n

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\"open<\/figure>\n\n\n\n

Open source<\/h4>\n\n\n\n

HI-ML is open source, based on PyTorch, and released under an MIT license.<\/p>\n<\/div>\n\n\n\n

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\"Easy<\/figure>\n\n\n\n

Easy to use<\/h4>\n\n\n\n

Makes building healthcare ML models easier, increasing productivity of scientists, clinicians, and engineers.<\/p>\n<\/div>\n\n\n\n

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\"scalable<\/figure>\n\n\n\n

Scalable<\/h4>\n\n\n\n

Uses Microsoft Azure to train models at scale using the latest GPU technology.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Research applications<\/h2>\n\n\n\n

HI-ML can be used to make ML model development easier for a variety different health and life sciences research applications, including:<\/p>\n\n\n\n

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Multi-modal radiology<\/h3>\n\n\n\n

HI-ML makes it easy to work with multimodal image & text data, such as chest x-ray images and associated radiological reports. We have released pre-trained ML models on Hugging Face (opens in new tab)<\/span><\/a> and associated datasets on PhysioNet (opens in new tab)<\/span><\/a> as part of our ECCV 2022 paper “Making the Most of Text Semantics to Improve Biomedical Vision–Language Processing<\/a><\/em>“<\/p>\n<\/div>\n\n\n\n

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Histopathology <\/h3>\n\n\n\n

HI-ML provides dedicated tools for end-to-end histopathology ML model development, testing, and visualization in Microsoft Azure.<\/p>\n<\/div>\n<\/div>\t\t<\/div>\n\t<\/div>\n\n\t<\/div>\n\n\n\n

Open-source toolkits and components<\/h2>\n\n\n\n

The HI-ML OSS toolbox comprises several packages and components to increase the productivity of health and life science researchers and developers. You can find out more on Read the Docs (opens in new tab)<\/span><\/a>. 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)<\/span><\/a>. We monitor these issues and will look to respond via GitHub.<\/p>\n\n\n\n

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HI-ML (opens in new tab)<\/span><\/a><\/h4>\n\n\n\n

Python package providing ML components for healthcare machine learning.<\/p>\n\n\n\n

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GitHub<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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HI-ML-Azure (opens in new tab)<\/span><\/a><\/h4>\n\n\n\n

Python package providing helper functions for running in Azure Machine Learning.<\/p>\n\n\n\n

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GitHub<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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HI-ML-cpath (opens in new tab)<\/span><\/a><\/h4>\n\n\n\n

Python package and ML model training workflows for working with histopathology images.<\/p>\n\n\n\n

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GitHub<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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HI-ML-multimodal<\/h4>\n\n\n\n

Python package for working with multi-modal health data.<\/p>\n\n\n\n

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GitHub<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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Who can benefit from the HI-ML OSS toolbox?<\/h2>\n\n\n\n