  Generative adversarial networks (GANs) offer an effective solution to the
image-to-image translation problem, thereby allowing for new possibilities in
medical imaging. They can translate images from one imaging modality to another
at a low cost. For unpaired datasets, they rely mostly on cycle loss. Despite
its effectiveness in learning the underlying data distribution, it can lead to
a discrepancy between input and output data. The purpose of this work is to
investigate the hypothesis that we can predict image quality based on its
latent representation in the GANs bottleneck. We achieve this by corrupting the
latent representation with noise and generating multiple outputs. The degree of
differences between them is interpreted as the strength of the representation:
the more robust the latent representation, the fewer changes in the output
image the corruption causes. Our results demonstrate that our proposed method
has the ability to i) predict uncertain parts of synthesized images, and ii)
identify samples that may not be reliable for downstream tasks, e.g., liver
segmentation task.
