  Although generative adversarial networks (GANs) have shown promise in medical
imaging, they have four main limitations that impeded their utility:
computational cost, data requirements, reliable evaluation measures, and
training complexity. Our work investigates each of these obstacles in a novel
application of StyleGAN2-ADA to high-resolution medical imaging datasets. Our
dataset is comprised of liver-containing axial slices from non-contrast and
contrast-enhanced computed tomography (CT) scans. Additionally, we utilized
four public datasets composed of various imaging modalities. We trained a
StyleGAN2 network with transfer learning (from the Flickr-Faces-HQ dataset) and
data augmentation (horizontal flipping and adaptive discriminator
augmentation). The network's generative quality was measured quantitatively
with the Fr\'echet Inception Distance (FID) and qualitatively with a visual
Turing test given to seven radiologists and radiation oncologists.
  The StyleGAN2-ADA network achieved a FID of 5.22 ($\pm$ 0.17) on our liver CT
dataset. It also set new record FIDs of 10.78, 3.52, 21.17, and 5.39 on the
publicly available SLIVER07, ChestX-ray14, ACDC, and Medical Segmentation
Decathlon (brain tumors) datasets. In the visual Turing test, the clinicians
rated generated images as real 42% of the time, approaching random guessing.
Our computational ablation study revealed that transfer learning and data
augmentation stabilize training and improve the perceptual quality of the
generated images. We observed the FID to be consistent with human perceptual
evaluation of medical images. Finally, our work found that StyleGAN2-ADA
consistently produces high-quality results without hyperparameter searches or
retraining.
