  Deep learning approaches to breast cancer detection in mammograms have
recently shown promising results. However, such models are constrained by the
limited size of publicly available mammography datasets, in large part due to
privacy concerns and the high cost of generating expert annotations. Limited
dataset size is further exacerbated by substantial class imbalance since
"normal" images dramatically outnumber those with findings. Given the rapid
progress of generative models in synthesizing realistic images, and the known
effectiveness of simple data augmentation techniques (e.g. horizontal
flipping), we ask if it is possible to synthetically augment mammogram datasets
using generative adversarial networks (GANs). We train a class-conditional GAN
to perform contextual in-filling, which we then use to synthesize lesions onto
healthy screening mammograms. First, we show that GANs are capable of
generating high-resolution synthetic mammogram patches. Next, we experimentally
evaluate using the augmented dataset to improve breast cancer classification
performance. We observe that a ResNet-50 classifier trained with GAN-augmented
training data produces a higher AUROC compared to the same model trained only
on traditionally augmented data, demonstrating the potential of our approach.
