  The ability to generate synthetic medical images is useful for data
augmentation, domain transfer, and out-of-distribution detection. However,
generating realistic, high-resolution medical images is challenging,
particularly for Full Field Digital Mammograms (FFDM), due to the textural
heterogeneity, fine structural details and specific tissue properties. In this
paper, we explore the use of progressively trained generative adversarial
networks (GANs) to synthesize mammograms, overcoming the underlying
instabilities when training such adversarial models. This work is the first to
show that generation of realistic synthetic medical images is feasible at up to
1280x1024 pixels, the highest resolution achieved for medical image synthesis,
enabling visualizations within standard mammographic hanging protocols. We hope
this work can serve as a useful guide and facilitate further research on GANs
in the medical imaging domain.
