关键词: 病理图像, 图卷积, GraphSAGE, 图池化

Abstract:

There are two problems in traditional Convolutional Neural Network（CNN） classification of histopathological images, on the one hand, limited by the size of CPU, CNN can not directly train high-resolution pathological images, which inevitably destroys the spatial structure information between cells and cannot learn global feature information. On the other hand, normal cells and cancerous cells in pathological images have their own pathological image features and have certain spatial correlation, but they cannot be fully expressed in structured two-dimensional array images. This paper proposes a histopathological images classification method based on Cell Graph Convolutional Network（C-GCN）. The high-resolution pathological image is transformed into graph structure. The GraphSAGE（Graph SAmple and aggregate） module is combined with graph pooling in the traditional GCN to extract more representative general features, so that C-GCN can directly learn features from high-resolution histological images and improve the robustness of the model.

Key words: histopathological image, graph convolutional, GraphSAGE（Graph SAmple and aggregate）, graph pooling