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Evaluating the Performance of StyleGAN2-ADA on Medical Images. (arXiv:2210.03786v1 [cs.CV])
Oct. 11, 2022, 1:12 a.m. | McKell Woodland, John Wood, Brian M. Anderson, Suprateek Kundu, Ethan Lin, Eugene Koay, Bruno Odisio, Caroline Chung, Hyunseon Christine Kang, Aradhan
cs.LG updates on arXiv.org arxiv.org
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 …
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