Graph Convolutional Neural Networks to Classify Whole Slide Images

Conventional biomedical measurement and tests used for patient diagnosis are increasingly digitized in the current clinical practice, which provides great opportunities for researchers to build smart computer-aided decision support systems using advanced data analytics. In this paper, we discuss how to build such systems for whole slide images (WSIs), the digitization of entire histology slides used by pathologists. While conventional techniques use grid-style-tile-based processing strategy with extracted features aggregated based on first-order statistics to detect regions of interest such as cancerous regions, they ignore the spatial relationships between nearby tiles: the closer the two tiles are spatially, the more similar they are in disease conditions. To capture such spatial proximity information, we present a novel application of graph convolutional networks (GCNs) to analyze WSIs. We model each tile of a WSI as a node in a graph, and apply GCNs to the resulting graph to aggregate the features for final classification. We use two histopathological image classification datasets, a breast cancer pathological dataset of 58 total samples with 36 benign ones, and another colon cancer dataset of 100 H&E images with 49 benign ones. The diagnosis accuracy achieved by GCN is consistently better than that achieved by conventional methods. The experiments results showed the potential of graph-based neural networks to improve biomedical imaging data analysis.