An optimization-based deep learning pipeline selects informative patches from H&E whole-slide images to classify breast cancer into PAM50 subtypes, achieving F1 scores of 0.88 internally and 0.80 externally.
Predicting Breast Cancer Gene Expression Signature by Applying Deep Convolutional Neural Networks From Unannotated Pathological Images
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A deep learning pipeline for PAM50 subtype classification using histopathology images and multi-objective patch selection
An optimization-based deep learning pipeline selects informative patches from H&E whole-slide images to classify breast cancer into PAM50 subtypes, achieving F1 scores of 0.88 internally and 0.80 externally.