  Sharing data from clinical studies can facilitate innovative data-driven
research and ultimately lead to better public health. However, sharing
biomedical data can put sensitive personal information at risk. This is usually
solved by anonymization, which is a slow and expensive process. An alternative
to anonymization is sharing a synthetic dataset that bears a behaviour similar
to the real data but preserves privacy. As part of the collaboration between
Novartis and the Oxford Big Data Institute, we generate a synthetic dataset
based on COSENTYX (secukinumab) Ankylosing Spondylitis clinical study. We apply
an Auxiliary Classifier GAN to generate synthetic MRIs of vertebral units. The
images are conditioned on the VU location (cervical, thoracic and lumbar). In
this paper, we present a method for generating a synthetic dataset and conduct
an in-depth analysis on its properties along three key metrics: image fidelity,
sample diversity and dataset privacy.
