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Screening mammography is an important front-line tool for the early detectionof breast cancer, and some 39 million exams are conducted each year in theUnited States alone. Here, we describe a multi-scale convolutional neuralnetwork (CNN) trained with a curriculum learning strategy that achieves highlevels of accuracy in classifying mammograms. Specifically, we first trainCNN-based patch classifiers on segmentation masks of lesions in mammograms, andthen use the learned features to initialize a scanning-based model that rendersa decision on the whole image, trained end-to-end on outcome data. Wedemonstrate that our approach effectively handles the "needle in a haystack"nature of full-image mammogram classification, achieving 0.92 AUROC on the DDSMdataset.

A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification