Ms-Gwnn: Multi-Scale Graph Wavelet Neural Network for Breast Cancer Diagnosis
Mo Zhang, Bin Dong, Quanzheng Li
Abstract
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.