MS-GWNN:multi-scale graph wavelet neural network for breast cancer diagnosis
This work aims to improve the accuracy of computer-aided breast cancer diagnosis for medical professionals, representing an incremental advancement in medical image analysis.
This paper addresses breast cancer diagnosis from histopathological images by proposing a multi-scale graph wavelet neural network (MS-GWNN). The method captures multi-scale contextual features using spectral graph wavelets, demonstrating superior performance on two public datasets.
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.