Evaluating the Performance of StyleGAN2-ADA on Medical Images. (arXiv:2210.03786v1 [cs.CV])

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 …

arxiv images medical performance stylegan2