  Chest x-rays are a vital tool in the workup of many patients. Similar to most
medical imaging modalities, they are profoundly multi-modal and are capable of
visualising a variety of combinations of conditions. There is an ever pressing
need for greater quantities of labelled data to develop new diagnostic tools,
however this is in direct opposition to concerns regarding patient
confidentiality which constrains access through permission requests and ethics
approvals. Previous work has sought to address these concerns by creating
class-specific GANs that synthesise images to augment training data. These
approaches cannot be scaled as they introduce computational trade offs between
model size and class number which places fixed limits on the quality that such
generates can achieve. We address this concern by introducing latent class
optimisation which enables efficient, multi-modal sampling from a GAN and with
which we synthesise a large archive of labelled generates. We apply a PGGAN to
the task of unsupervised x-ray synthesis and have radiologists evaluate the
clinical realism of the resultant samples. We provide an in depth review of the
properties of varying pathologies seen on generates as well as an overview of
the extent of disease diversity captured by the model. We validate the
application of the Fr\'echet Inception Distance (FID) to measure the quality of
x-ray generates and find that they are similar to other high resolution tasks.
We quantify x-ray clinical realism by asking radiologists to distinguish
between real and fake scans and find that generates are more likely to be
classed as real than by chance, but there is still progress required to achieve
true realism. We confirm these findings by evaluating synthetic classification
model performance on real scans. We conclude by discussing the limitations of
PGGAN generates and how to achieve controllable, realistic generates.
