  Data scarcity and class imbalance are two fundamental challenges in many
machine learning applications to healthcare. Breast cancer classification in
mammography exemplifies these challenges, with a malignancy rate of around 0.5%
in a screening population, which is compounded by the relatively small size of
lesions (~1% of the image) in malignant cases. Simultaneously, the prevalence
of screening mammography creates a potential abundance of non-cancer exams to
use for training. Altogether, these characteristics lead to overfitting on
cancer cases, while under-utilizing non-cancer data. Here, we present a novel
generative adversarial network (GAN) model for data augmentation that can
realistically synthesize and remove lesions on mammograms. With self-attention
and semi-supervised learning components, the U-net-based architecture can
generate high resolution (256x256px) outputs, as necessary for mammography.
When augmenting the original training set with the GAN-generated samples, we
find a significant improvement in malignancy classification performance on a
test set of real mammogram patches. Overall, the empirical results of our
algorithm and the relevance to other medical imaging paradigms point to
potentially fruitful further applications.
