{"id":740356,"date":"2021-07-01T07:14:35","date_gmt":"2021-07-01T14:14:35","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&p=740356"},"modified":"2022-11-17T01:37:03","modified_gmt":"2022-11-17T09:37:03","slug":"project-innereye-open-source-software-for-medical-imaging-ai","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/project-innereye-open-source-software-for-medical-imaging-ai\/","title":{"rendered":"Project InnerEye Open-Source Software for Medical Imaging AI"},"content":{"rendered":"
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Project InnerEye Open-Source Software for Medical Imaging AI<\/h1>\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

Project InnerEye open-source software (OSS) is created and used for deep learning research by the Project InnerEye team<\/a> in Microsoft Health Futures<\/a>. It is released at no-cost under an MIT open-source license to make it widely available for the global medical imaging community, who can leverage our work. The tools aim to increase productivity for research and development of best-in-class medical imaging AI and help to enable deployment using Microsoft Azure cloud computing (subject to appropriate regulatory approvals). Support for these OSS tools is via GitHub Issues on the relevant repositories.<\/p>\n\n\n\n

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\"computer<\/figure>\n\n\n\n

Open source<\/h3>\n\n\n\n

Project InnerEye toolkits are open-source, based on PyTorch, and released under an MIT license<\/p>\n<\/div>\n\n\n\n

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\"four<\/figure>\n\n\n\n

Easy to use<\/h3>\n\n\n\n

Makes building medical imaging models easier, increasing productivity of research and developers<\/p>\n<\/div>\n\n\n\n

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\"box<\/figure>\n\n\n\n

Scalable<\/h3>\n\n\n\n

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

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\"laptop<\/figure>\n\n\n\n

Deployable<\/h3>\n\n\n\n

OSS components to help deploy your ML models within existing medical imaging workflows<\/p>\n<\/div>\n\n\n\n

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\"cog<\/figure>\n\n\n\n

Best practices<\/h3>\n\n\n\n

Makes it easy to follow best practices when developing and maintaining your AI models<\/p>\n<\/div>\n\n\n\n

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\"clipboard<\/figure>\n\n\n\n

Peer-reviewed<\/h3>\n\n\n\n

Peer-reviewed research validation of ML models for radiation therapy planning workflows using CT images<\/p>\n<\/div>\n<\/div>\n\n\n\n

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

There are several InnerEye OSS tools to help with medical imaging AI research and development. Use the links below to learn more about each tool and go to the respective GitHub repositories. The Getting Started<\/a> page on this site has more details of how these tools might be used together for radiation therapy workflow planning. 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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InnerEye-DeepLearning Toolkit<\/a><\/h4>\n\n\n\n

Train PyTorch-based medical imaging models at scale on Microsoft Azure. This includes the ability to bring any PyTorch Lightning model and get cloud scaling out-of-the-box.<\/p>\n\n\n\n

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GitHub<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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InnerEye-Gateway<\/a><\/h4>\n\n\n\n

Manage image de-identification and transfer of images from a hospital network to and from Microsoft Azure for running inference, in a secure way.<\/p>\n\n\n\n

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GitHub<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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InnerEye-Inference<\/a><\/h4>\n\n\n\n

Run inference on medical imaging ML models trained with the InnerEye-DeepLearning toolkit.<\/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 Project InnerEye Open-Source Tools?<\/h2>\n\n\n\n