Fluorescence microscopy and histopathology image based cancer classification using graph convolutional network with channel splitting

Abstract

Since the proliferation of deep learning, several convolutional neural networks (CNNs) are developed to attain significant breakthroughs for automated cancer classification using histopathology and fluorescence microscopy images. This work enhances the classification performances of human breast and lung-colon cancers further by exploring a two-layer graph convolutional network (GCN) upon a proposed lightweight deep convolutional backbone or existing pre-trained CNN. The first graph convolution layer considers local regions as the graph nodes with channel information as node features. The second layer is rendered by pooling and splitting the output feature map of former layer into a low dimensional feature vector that serves as node features. The proposed method, named Channel-Splitting Graph Convolutional Network (CS-GCN), enhances holistic feature representation of spatial structural information. The significance of region-aware distinctness is explored for building a correlation among neighboring regions through node-level mixed feature propagation of a graph. The experiments are carried out on three public datasets, representing the breast cancer (actin-labeled fluorescence microscopy image dataset (FMID), and BreakHis dataset with four magnifications), and lung-colon cancer (LC25000 dataset). The top-1 classification accuracies attained by CS-GCN using ResNet-50 backbone on the FMID: 99.30%, BreakHis 40x: 98.0%, BreakHis 100x: 97.81%, BreakHis 200x: 97.33%, BreakHis 400x: 96.85%, and LC25000: 100.0%. The performances are improved on these datasets, while built upon a proposed convolutional stem as well as pre-trained ResNet-50 and DenseNet-201 backbones, implying the effectiveness of the proposed CS-GCN.