Representation learning for Whole Slide Images (WSIs) is pivotal in developing image-based systems to achieve higher precision in diagnostic pathology. The authors propose a two-stage framework for WSI representation learning. They use graph neural networks to learn relations among sampled representative patches to aggregate the image information into a single vector representation. They also introduce attention via graph pooling to automatically infer patches with higher relevance. The authors experiment on 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% and AUC of 0.89 on lung cancer sub-type classification by extracting features from a pre-trained DenseNet model. The authors will be presenting this work at CVPR 2020!