No-code machine learning in radiology: implementation and validation of a platform that allows clinicians to train their own models
Daniel C. Elton,
Giridhar Dasegowda,
James Y. Sato
et al.
Abstract:Machine learning models can assist clinicians and researchers in many tasks within radiology such as diagnosis, triage, segmentation/measurement, and quality assurance. To better leverage machine learning we have developed a platform that allows users to label data and train models without requiring any programming knowledge. The technology stack consists of a TypeScript web application running on .NET for user interaction, Python, PyTorch, and MONAI for machine learning, DICOM WADO-RS to retrieve data from cl… Show more
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