  We propose a novel framework for controllable pathological image synthesis
for data augmentation. Inspired by CycleGAN, we perform cycle-consistent
image-to-image translation between two domains: healthy and pathological.
Guided by a semantic mask, an adversarially trained generator synthesizes
pathology on a healthy image in the specified location. We demonstrate our
approach on an institutional dataset of cerebral microbleeds in traumatic brain
injury patients. We utilize synthetic images generated with our method for data
augmentation in cerebral microbleeds detection. Enriching the training dataset
with synthetic images exhibits the potential to increase detection performance
for cerebral microbleeds in traumatic brain injury patients.
