  Medical students and radiology trainees typically view thousands of images in
order to "train their eye" to detect the subtle visual patterns necessary for
diagnosis. Nevertheless, infrastructural and legal constraints often make it
difficult to access and quickly query an abundance of images with a
user-specified feature set. In this paper, we use a conditional generative
adversarial network (GAN) to synthesize $1024\times1024$ pixel pelvic
radiographs that can be queried with conditioning on fracture status. We
demonstrate that the conditional GAN learns features that distinguish fractures
from non-fractures by training a convolutional neural network exclusively on
images sampled from the GAN and achieving an AUC of $>0.95$ on a held-out set
of real images. We conduct additional analysis of the images sampled from the
GAN and describe ongoing work to validate educational efficacy.
