  The current state-of-the art techniques for image segmentation are often
based on U-Net architectures, a U-shaped encoder-decoder networks with skip
connections. Despite the powerful performance, the architecture often does not
perform well when used on data which has different characteristics than the
data it was trained on. Many techniques for improving performance in the
presence of domain shift have been developed, however typically only have loose
connections to the theory of domain adaption. In this work, we propose an
unsupervised domain adaptation framework for U-Nets with theoretical guarantees
based on the Margin Disparity Discrepancy [1] called the MDD-UNet. We evaluate
the proposed technique on the task of hippocampus segmentation, and find that
the MDD-UNet is able to learn features which are domain-invariant with no
knowledge about the labels in the target domain. The MDD-UNet improves
performance over the standard U-Net on 11 out of 12 combinations of datasets.
This work serves as a proof of concept by demonstrating an improvement on the
U-Net in it's standard form without modern enhancements, which opens up a new
avenue of studying domain adaptation for models with very large hypothesis
spaces from both methodological and practical perspectives. Code is available
at https://github.com/asbjrnmunk/mdd-unet.
