  During their formative years, radiology trainees are required to interpret
hundreds of mammograms per month, with the objective of becoming apt at
discerning the subtle patterns differentiating benign from malignant lesions.
Unfortunately, medico-legal and technical hurdles make it difficult to access
and query medical images for training.
  In this paper we train a generative adversarial network (GAN) to synthesize
512 x 512 high-resolution mammograms. The resulting model leads to the
unsupervised separation of high-level features (e.g. the standard mammography
views and the nature of the breast lesions), with stochastic variation in the
generated images (e.g. breast adipose tissue, calcification), enabling
user-controlled global and local attribute-editing of the synthesized images.
  We demonstrate the model's ability to generate anatomically and medically
relevant mammograms by achieving an average AUC of 0.54 in a double-blind study
on four expert mammography radiologists to distinguish between generated and
real images, ascribing to the high visual quality of the synthesized and edited
mammograms, and to their potential use in advancing and facilitating medical
education.
