GQ-GCN: Group Quadratic Graph Convolutional Network for Classification of Histopathological Images

Abstract

Convolutional neural network (CNN) has achieved superior performance on the computer-aided diagnosis for histopathological images. Although the spatial arrangement of cells of various types in histopathological images is an important characteristic for the diagnosis of cancers, CNN cannot explicitly capture this spatial structure information. This challenge can be overcome by constructing the graph data on histopathological images and learning the graph representation with valuable spatial correlations in the graph convolutional network (GCN). However, the current GCN models for histopathological images usually require a complicated preprocessing process or prior experience of node selection for graph construction. Moreover, there is a lack of learning architecture that can perform feature selection to refine features in the GCN. In this work, we propose a group quadratic graph convolutional network (GQ-GCN), which adopts CNN to extract features from histopathological images for further adaptively graph construction. In particular, the group graph convolutional network (G-GCN) is developed to implement both feature selection and compression of graph representation. In addition, the quadratic operation is specifically embedded into the graph convolution to enhance the representation ability of a single neuron for complex data. The experimental results on two public breast histopathological image datasets indicate the effectiveness of the proposed GQ-GCN.