  In medical image segmentation, domain generalization poses a significant
challenge due to domain shifts caused by variations in data acquisition devices
and other factors. These shifts are particularly pronounced in the most common
scenario, which involves only single-source domain data due to privacy
concerns. To address this, we draw inspiration from the self-supervised
learning paradigm that effectively discourages overfitting to the source
domain. We propose the Denoising Y-Net (DeY-Net), a novel approach
incorporating an auxiliary denoising decoder into the basic U-Net architecture.
The auxiliary decoder aims to perform denoising training, augmenting the
domain-invariant representation that facilitates domain generalization.
Furthermore, this paradigm provides the potential to utilize unlabeled data.
Building upon denoising training, we propose Denoising Test Time Adaptation
(DeTTA) that further: (i) adapts the model to the target domain in a
sample-wise manner, and (ii) adapts to the noise-corrupted input. Extensive
experiments conducted on widely-adopted liver segmentation benchmarks
demonstrate significant domain generalization improvements over our baseline
and state-of-the-art results compared to other methods. Code is available at
https://github.com/WenRuxue/DeTTA.
