of the entire breast. For example, a full-field digital mammography (FFDM) image is typically $4000 \times 3000$ pixels while a cancerous region of interest (ROI) can be as small as $100 \times 100$ pixels. If ROI annotations were widely available in mammography databases then established object detection and classification methods such as the region-based convolutional neural network (R-CNN) [@girshick_rich_2014] and its variants [@girshick_fast_2015; @ren_faster_2015; @dai_r-fcn_2016] could be readily applied. However, approaches that require ROI annotations [@jamieson_breast_2012; @arevalo_convolutional_2015; @carneiro_unregistered_2015; @dhungel_automated_2015; @ertosun_probabilistic_2015; @akselrod-ballin_region_2016; @arevalo_representation_2016; @levy_breast_2016; @dhungel_automated_2016; @becker_deep_2017; @ribli_detecting_2017] often cannot be transferred to large mammography databases that lack ROI annotations, which are laborious and costly to assemble. Indeed, few public mammography databases are annotated [@moreira_inbreast_2012]. Yet, deep learning requires large training datasets to be most effective. Thus, it is essential to leverage both the few fully annotated datasets, as well as larger datasets labeled with only the cancer status of each image to improve the accuracy of breast cancer classification algorithms.

Pre-training is a promising method to address the training problem. For example, Hinton et al. [@hinton_fast_2006] used layer-wise pre-training to initialize the weight parameters of a deep belief net (DBN) with three hidden layers and then fine-tuned it for classification. They 