MS-GWNN:multi-scale graph wavelet neural network for breast cancer diagnosis

• URL: http://arxiv.org/abs/2012.14619v1
• Date: Tue, 29 Dec 2020 06:04:27 GMT
• Title: MS-GWNN:multi-scale graph wavelet neural network for breast cancer diagnosis
• Authors: Mo Zhang, Quanzheng Li
• Abstract summary: It is crucial to take multi-scale information of tissue structure into account in the detection of breast cancer. In this work, we present a novel graph convolutional neural network for his image classification of breast cancer.
• Score: 8.679247709183567
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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