Contrastive learning-based computational histopathology predict differential expression of cancer driver genes. (arXiv:2204.11994v1 [cs.CV])

Digital pathological analysis is run as the main examination used for cancer
diagnosis. Recently, deep learning-driven feature extraction from pathology
images is able to detect genetic variations and tumor environment, but few
studies focus on differential gene expression in tumor cells. In this paper, we
propose a self-supervised contrastive learning framework, HistCode, to infer
differential gene expressions from whole slide images (WSIs). We leveraged
contrastive learning on large-scale unannotated WSIs to derive slide-level
histopathological feature in latent space, and then …

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