Multiple Instance Classification for Gastric Cancer Pathological Images Based on Implicit Spatial Topological Structure Representation

Abstract

Gastric cancer is a malignant tumor with high incidence. Computer-aided screening systems for gastric cancer pathological images can contribute to reducing the workload of specialists and improve the efficiency of disease diagnosis. Due to the high resolution of images, it is common to divide the whole slide image (WSI) into a set of image patches with overlap before utilizing deep neural networks for further analysis. However, not all patches split from the same cancerous WSI contain information of cancerous issues. This restriction naturally satisfies the assumptions of multiple instance learning (MIL). Moreover, the spatial topological structure relationships between local areas in a WSI are destroyed in the process of patch partitioning. Most existing multiple instance classification (MIC) methods fail to take into account the topological relationships between instances. In this paper, we propose a novel multiple instance classification framework based on graph convolutional networks (GCNs) for gastric microscope image classification. Firstly, patch embeddings were generated by feature extraction. Then, a graph structure was introduced to model the spatial topological structure relationships between instances. Additionally, a graph classification model with hierarchical pooling was constructed to achieve this multiple instance classification task. To certify the effectiveness and generalization of our method, we conducted comparative experiments on two different modes of gastric cancer pathological image datasets. The proposed method achieved average fivefold cross-validation precisions of 91.16% and 98.26% for gastric cancer classification on the two datasets, respectively.

Keywords

• gastric pathological images classification
• graph convolutional networks
• multiple instance learning