  Generative Adversarial Networks (GANs) have become increasingly powerful,
generating mind-blowing photorealistic images that mimic the content of
datasets they were trained to replicate. One recurrent theme in medical imaging
is whether GANs can also be effective at generating workable medical data as
they are for generating realistic RGB images. In this paper, we perform a
multi-GAN and multi-application study to gauge the benefits of GANs in medical
imaging. We tested various GAN architectures from basic DCGAN to more
sophisticated style-based GANs on three medical imaging modalities and organs
namely : cardiac cine-MRI, liver CT and RGB retina images. GANs were trained on
well-known and widely utilized datasets from which their FID score were
computed to measure the visual acuity of their generated images. We further
tested their usefulness by measuring the segmentation accuracy of a U-Net
trained on these generated images.
  Results reveal that GANs are far from being equal as some are ill-suited for
medical imaging applications while others are much better off. The
top-performing GANs are capable of generating realistic-looking medical images
by FID standards that can fool trained experts in a visual Turing test and
comply to some metrics. However, segmentation results suggests that no GAN is
capable of reproducing the full richness of a medical datasets.
