Representation Learning of Histopathology Images using Graph Neural Networks
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Representation learning for Whole Slide Images (WSIs) is pivotal in developing image-based systems to achieve higher precision in diagnostic pathology. We propose a two-stage framework for WSI representation learning. We sample relevant patches using a color-based method and use graph neural networks to learn relations among sampled patches to aggregate the image information into a single vector representation. We introduce attention via graph pooling to automatically infer patches with higher relevance. We demonstrate the performance of our approach for discriminating two sub-types of lung cancers, Lung Adenocarcinoma (LUAD) Lung Squamous Cell Carcinoma (LUSC). We collected 1,026 lung cancer WSIs with the 40× magnification from The Cancer Genome Atlas (TCGA) dataset, the largest public repository of histopathology images and achieved state-of-the-art accuracy of 88.8 of 0.89 on lung cancer sub-type classification by extracting features from a pre-trained DenseNet
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