Classification of Microcalcification Clusters Using Bilateral Features Based on Graph Convolutional Network

Breast cancer is one of the diseases with the highest incidence and mortality among women in the world, which has posed a serious threat to women's health. The appearance of clustered calcifications is one of the important signs of breast cancer, and thus how to classify clustered calcifications comes to be a key breakthrough in controlling breast cancer. In this study, the discriminant model based on image convolution is used to learn the image features related to the classification of clustered microcalcifications, and the graph convolutional network (GCN) based on topological graph is used to learn the spatial distribution characteristics of clustered microcalcifications. These two models are fused to obtain a complementary model of image information and spatial information. The results show that the performance of the fusion model proposed in this paper is obviously superior to that of the two classification models in the classification of clustered microcalcification.