  Accurate and robust medical image segmentation is fundamental and crucial for
enhancing the autonomy of computer-aided diagnosis and intervention systems.
Medical data collection normally involves different scanners, protocols, and
populations, making domain adaptation (DA) a highly demanding research field to
alleviate model degradation in the deployment site. To preserve the model
performance across multiple testing domains, this work proposes the
Curriculum-based Augmented Fourier Domain Adaptation (Curri-AFDA) for robust
medical image segmentation. In particular, our curriculum learning strategy is
based on the causal relationship of a model under different levels of data
shift in the deployment phase, where the higher the shift is, the harder to
recognize the variance. Considering this, we progressively introduce more
amplitude information from the target domain to the source domain in the
frequency space during the curriculum-style training to smoothly schedule the
semantic knowledge transfer in an easier-to-harder manner. Besides, we
incorporate the training-time chained augmentation mixing to help expand the
data distributions while preserving the domain-invariant semantics, which is
beneficial for the acquired model to be more robust and generalize better to
unseen domains. Extensive experiments on two segmentation tasks of Retina and
Nuclei collected from multiple sites and scanners suggest that our proposed
method yields superior adaptation and generalization performance. Meanwhile,
our approach proves to be more robust under various corruption types and
increasing severity levels. In addition, we show our method is also beneficial
in the domain-adaptive classification task with skin lesion datasets. The code
is available at https://github.com/lofrienger/Curri-AFDA.
