  Recently, paired (e.g. Pix2pix) and unpaired (e.g. CycleGAN) image-to-image
translation methods have shown effective in medical imaging tasks. In practice,
however, it can be difficult to apply these deep models on medical data
volumes, such as from MR and CT scans, since such data volumes tend to be
3-dimensional and of a high spatial resolution pushing the limits of the memory
constraints of GPU hardware that is typically used to train these models.
Recent studies in the field of invertible neural networks have shown that
reversible neural networks do not require to store intermediate activations for
training. We use the RevGAN model that makes use of this property to perform
memory-efficient partially-reversible image-to-image translation. We
demonstrate this by performing a 3D super-resolution and a 3D domain-adaptation
task on chest CT data volumes.
