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Breast cancer is one of the most common cancers worldwide, and early detection can significantly reduce its mortality rate. It is crucial to take multi-scale information of tissue structure into account in the detection of breast cancer. And thus, it is the key to design an accurate computer-aided detection (CAD) system to capture multi-scale contextual features in a cancerous tissue. In this work, we present a novel graph convolutional neural network for histopathological image classification of breast cancer. The new method, named multi-scale graph wavelet neural network (MS-GWNN), leverages the localization property of spectral graph wavelet to perform multi-scale analysis. By aggregating features at different scales, MS-GWNN can encode the multi-scale contextual interactions in the whole pathological slide. Experimental results on two public datasets demonstrate the superiority of MS-GWNN. Moreover, ablation studies show that multi-scale analysis has a significant impact on the accuracy of cancer diagnosis.

Ms-Gwnn: Multi-Scale Graph Wavelet Neural Network for Breast Cancer Diagnosis