Assessing the ability of generative adversarial networks to learn canonical medical image statistics. (arXiv:2204.12007v1 [eess.IV])

In recent years, generative adversarial networks (GANs) have gained
tremendous popularity for potential applications in medical imaging, such as
medical image synthesis, restoration, reconstruction, translation, as well as
objective image quality assessment. Despite the impressive progress in
generating high-resolution, perceptually realistic images, it is not clear if
modern GANs reliably learn the statistics that are meaningful to a downstream
medical imaging application. In this work, the ability of a state-of-the-art
GAN to learn the statistics of canonical stochastic image models …

arxiv canonical generative adversarial networks image medical networks statistics