jamesdolezal/biscuit
Bayesian Inference of Slide-level Confidence via Uncertainty Index Thresholding
BISCUIT helps pathology researchers analyze whole-slide images (WSIs) for cancer diagnosis, specifically lung adenocarcinoma vs. squamous cell carcinoma. It takes raw WSI files and outputs classifications with an important confidence score, allowing researchers to distinguish between highly reliable and uncertain predictions. This tool is for pathologists, oncologists, and medical researchers working with digital histopathology images.
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Use this if you need to classify whole-slide images for cancer diagnosis and want to know how confident the AI model is in its prediction, allowing you to prioritize cases needing human review.
Not ideal if you are not working with whole-slide pathology images or if you don't need uncertainty quantification for your classification tasks.
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30
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3
Language
Python
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Last pushed
Sep 06, 2024
Commits (30d)
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