Synthetic Medical Imaging Generation with Generative Adversarial
  Networks For Plain Radiographs
John R. McNulty Lee Kho Alexandria L. Case Charlie Fornaca Drew Johnston David Slater Joshua M. Abzug Sybil A. Russell
In medical imaging, access to data is commonly limited due to patient privacy
restrictions and the issue that it can be difficult to acquire enough data in
the case of rare diseases.[1] The purpose of this investigation was to develop
a reusable open-source synthetic image generation pipeline, the GAN Image
Synthesis Tool (GIST), that is easy to use as well as easy to deploy. The
pipeline helps to improve and standardize AI algorithms in the digital health
space by generating high quality synthetic image data that is not linked to
specific patients. Its image generation capabilities include the ability to
generate imaging of pathologies or injuries with low incidence rates. This
improvement of digital health AI algorithms could improve diagnostic accuracy,
aid in patient care, decrease medicolegal claims, and ultimately decrease the
overall cost of healthcare. The pipeline builds on existing Generative
Adversarial Networks (GANs) algorithms, and preprocessing and evaluation steps
were included for completeness. For this work, we focused on ensuring the
pipeline supports radiography, with a focus on synthetic knee and elbow x-ray
images. In designing the pipeline, we evaluated the performance of current GAN
architectures, studying the performance on available x-ray data. We show that
the pipeline is capable of generating high quality and clinically relevant
images based on a lay person's evaluation and the Fr\'echet Inception Distance
(FID) metric.