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Found 3 papers, 2 papers with code
WEEP: A method for spatial interpretation of weakly supervised CNN models in computational pathology
Deep learning enables the modelling of high-resolution histopathology whole-slide images (WSI).
Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unlabeled, unannotated pathology slides
Definitive cancer diagnosis and management depend upon the extraction of information from microscopy images by pathologists.
Using deep learning to detect patients at risk for prostate cancer despite benign biopsies
As a proof-of-principle, we developed and validated a deep convolutional neural network model to distinguish between morphological patterns in benign prostate biopsy whole slide images from men with and without established cancer.
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