  Deep generative models (DGMs) have the potential to revolutionize diagnostic
imaging. Generative adversarial networks (GANs) are one kind of DGM which are
widely employed. The overarching problem with deploying GANs, and other DGMs,
in any application that requires domain expertise in order to actually use the
generated images is that there generally is not adequate or automatic means of
assessing the domain-relevant quality of generated images. In this work, we
demonstrate several objective tests of images output by two popular GAN
architectures. We designed several stochastic context models (SCMs) of distinct
image features that can be recovered after generation by a trained GAN. Several
of these features are high-order, algorithmic pixel-arrangement rules which are
not readily expressed in covariance matrices. We designed and validated
statistical classifiers to detect specific effects of the known arrangement
rules. We then tested the rates at which two different GANs correctly
reproduced the feature context under a variety of training scenarios, and
degrees of feature-class similarity. We found that ensembles of generated
images can appear largely accurate visually, and show high accuracy in ensemble
measures, while not exhibiting the known spatial arrangements. Furthermore,
GANs trained on a spectrum of distinct spatial orders did not respect the given
prevalence of those orders in the training data. The main conclusion is that
SCMs can be engineered to quantify numerous errors, per image, that may not be
captured in ensemble statistics but plausibly can affect subsequent use of the
GAN-generated images.
