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Recently, paired (e.g. Pix2pix) and unpaired (e.g. CycleGAN) image-to-imagetranslation methods have shown effective in medical imaging tasks. In practice,however, it can be difficult to apply these deep models on medical datavolumes, such as from MR and CT scans, since such data volumes tend to be3-dimensional and of a high spatial resolution pushing the limits of the memoryconstraints of GPU hardware that is typically used to train these models.Recent studies in the field of invertible neural networks have shown thatreversible neural networks do not require to store intermediate activations fortraining. We use the RevGAN model that makes use of this property to performmemory-efficient partially-reversible image-to-image translation. Wedemonstrate this by performing a 3D super-resolution and a 3D domain-adaptationtask on chest CT data volumes.

Chest CT Super-resolution and Domain-adaptation using Memory-efficient 3D Reversible GANs