  Breast cancer is one of the most common cancers in women worldwide, and early
detection can significantly reduce the mortality rate of breast cancer. 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 the proposed method. Moreover, through ablation studies, we find
that multi-scale analysis has a significant impact on the accuracy of cancer
diagnosis.
