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Found 3 papers, 3 papers with code
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.
Adversarial learning of cancer tissue representations
We present an adversarial learning model to extract feature representations of cancer tissue, without the need for manual annotations.
Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue.
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