Representation Learning of Histopathology Images using Graph Neural Networks

• URL: http://arxiv.org/abs/2004.07399v2
• Date: Fri, 17 Apr 2020 16:39:26 GMT
• Title: Representation Learning of Histopathology Images using Graph Neural Networks
• Authors: Mohammed Adnan, Shivam Kalra, Hamid R. Tizhoosh
• Abstract summary: 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 demonstrate the performance of our approach for discriminating two sub-types of lung cancers, Lung Adenocarcinoma (LUAD) & Lung Squamous Cell Carcinoma (LUSC)
• Score: 12.427740549056288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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$\times$ 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% and AUC of 0.89 on lung cancer sub-type classification by extracting features from a pre-trained DenseNet

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