Graph Convolutional Neural Networks to Classify Whole Slide Images

Abstract

Whole slide images (WSIs) are the digitization of histology slides and are increasingly used by pathologists to detect the cancerous regions and make diagnosis for patients. Recent machine learning and deep learning algorithms have shown great success in automated classification of WSIs. However, given the computational challenge associated with processing high resolution WSIs, conventional techniques rely on a patch-based approach, and subsequently aggregate extracted features using firs-order statistics. However, as cancerous regions are clustered together, such an approach ignores the spatial relationships within each tile. Here, we present a novel application of graph convolutional networks (GCNs) to analyze WSIs. GCNs are powerful deep neural networks for modelling node relationships in a graph. To capture the spatial information, we model each tile as a node in the graph, and aggregate features by applying GCNs to the graph. The classification results on real-world histopathology datasets shows improved performance over conventional methods, and highlights the potential of graph-based methods in biomedical data analytics.