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This project uses a sample dataset of chest X-rays from the NIH to train a CNN model. The goal is for the model to learn how to detect anomolies and areas of interest.
Other components of the solution include:
- Sample EMR dashboard to view patient details
- Chestist web interface SMART on FHIR app to work with Chestist data in the context of a selected patient
- Imaging-API Azure Function to securely fetch images from an Azure Storage account
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.
If you are interested in projects like this or connecting with the health and life sciences developer community, please join our Discord server at https://aka.ms/HLS-Discord. We're a technology agnostic community seeking to share and collaborate on all things related to developing healthcare solutions. For in-depth questions specific to this project, please use the "Discussions" tab on GitHub. We welcome your thoughts and feedback.
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