screening. Breast cancer is the second leading cause of cancer deaths among U.S. women [@american_cancer_society_how_2018] and screening mammography has been found to reduce mortality [@oeffinger_breast_2015]. Despite the benefits, screening mammography is associated with a high risk of false positives as well as false negatives. According to a study conducted by the Breast Cancer Surveillance Consortium in 2009, the overall sensitivity of digital screening mammography in the U.S. is 84.4% and the overall specificity is 90.8% [@breast_cancer_surveillance_consortium_performance_2009]. To help radiologists improve the predictive accuracy of screening mammography, computer-assisted detection and diagnosis (CAD) software (reviewed in [@elter_cadx_2009]) have been developed and in clinical use since the 1990s. Unfortunately, data suggests that commercial CAD systems have not led to significant improvement in performance [@fenton_influence_2007; @cole_impact_2014; @lehman_diagnostic_2015] and progress has stagnated in the past decade. With the remarkable success of deep learning in visual object recognition and detection, and many other domains [@lecun_deep_2015], there is much interest in developing deep learning tools to assist radiologists and improve the accuracy of screening mammography [@aboutalib_deep_2018; @kim_applying_2018; @hamidinekoo_deep_2018; @burt_deep_2018].

Early detection of subclinical breast cancer on screening mammography is challenging as an image classification task because the tumors themselves occupy only a small portion of the image 