Multi-Scale Convolutional Neural Networks for Classification of Digital Mammograms with Breast Calcifications

Abstract

Breast cancer is the most commonly diagnosed cancer in women. Mammography is a widely used tool for breast cancer screening. Breast calcifications appear on mammograms as tiny white spots or grainy dots with a variety of clustering structures which may be time-consuming and difficult to identify by the human eye. Recent advances in multi-scale architectures have demonstrated strong feature representation for both large-scale contextual information and small-scale features. We hypothesized that the new architectures could be employed for classification of mammograms with breast calcifications and possibly for localization of breast calcification regions. In this study, we investigated different multi-scale architectures on 1,617 mammograms that contained only breast calcifications locally curated from our institution. Our best multi-scale attention network with hierarchical block-wise and layer-wise feature representation capability achieved an accuracy of 84.34% and an area under the receiver operating characteristic curve (AUROC) of 90.36%. Similar performance was observed across breast density categories. Our network simultaneously processes both craniocaudal (CC) and mediolateral oblique (MLO) views of the breast providing class activation maps that were able to precisely localize breast calcification regions in a weakly supervised manner, indicating the benefits of strong feature representation through the multi-scale architectures.