nspunn1993/BT-Unet
BT-Unet: A self-supervised learning framework
Automates the precise outlining of structures within medical images, a process known as segmentation. It takes in unlabeled biomedical images, learns their inherent features, and then, with a small set of labeled examples, accurately identifies regions of interest. This is ideal for medical researchers, clinicians, and biomedical analysts who need to efficiently process and interpret large volumes of imaging data.
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Use this if you need to perform accurate segmentation on biomedical images but have limited access to costly and time-consuming expert annotations.
Not ideal if you already possess a large, comprehensively annotated dataset for your specific segmentation task.
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Last pushed
Dec 05, 2022
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