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Deep Texture Representations as a Universal Encoder for Pan-cancer Histology
Cancer histological images contain rich biological and clinical information, but quantitative representation can be problematic and has prevented direct comparison and accumulation of large-scale datasets. Here we show that deep texture representations (DTRs) produced by a bilinear Convolutional Neural Network, express cancer morphology well in an unsupervised manner, and work as a universal encoder for cancer histology. DTRs are useful for content-based image retrieval, enabling quickdoi:10.1101/2020.07.28.224253 fatcat:6oklb4ttfzhkjbouvs42jxfrde