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

Tycho F.A. van der Ouderaa[1, 2]    Daniel E. Worrall[1]    Bram van Ginneken[2]

[1] University of Amsterdam, Amsterdam, The Netherlands
[2] Diagnostic Image Analysis Group, Radboud UMC, Nijmegen, The Netherlands

In MIDL 2019

Paper | Code | Poster


Medical imaging data are typically large in size. As a result, it can be difficult to train deep neural models on them. The activations of invertible neural networks do not have to be stored to perform backpropagation, therefore such networks can be used to save memory when handling large data volumes. We use a technique called additive coupling to obtain a memory-efficient partially-reversible image-to-image translation model. With this model, we perform a 3D super-resolution and 3D domain-adaptation task, on both paired and unpaired CT scan data. Additionally, we demonstrate experimentally that the model requires significantly less GPU memory than models without reversibility.


Link to Paper, MIDL 2019


A PyTorch implementation has been made available in this Github repository.
For questions about the code, please create a ticket on the github project or contact Tycho van der Ouderaa.

Related Work


We grateful to the Diagnostic Image Analysis Group (DIAG) of the Radboud University Medical Center, and in particular Prof. Dr. Bram van Ginneken for his collaboration on this project. We also thank the Netherlands Organisation for Scientific Research (NWO) for supporting this research and providing computational resources.