  Currently there is strong interest in data-driven approaches to medical image
classification. However, medical imaging data is scarce, expensive, and fraught
with legal concerns regarding patient privacy. Typical consent forms only allow
for patient data to be used in medical journals or education, meaning the
majority of medical data is inaccessible for general public research. We
propose a novel, two-stage pipeline for generating synthetic medical images
from a pair of generative adversarial networks, tested in practice on retinal
fundi images. We develop a hierarchical generation process to divide the
complex image generation task into two parts: geometry and photorealism. We
hope researchers will use our pipeline to bring private medical data into the
public domain, sparking growth in imaging tasks that have previously relied on
the hand-tuning of models. We have begun this initiative through the
development of SynthMed, an online repository for synthetic medical images.
