  For medical image analysis, segmentation models trained on one or several
domains lack generalization ability to unseen domains due to discrepancies
between different data acquisition policies. We argue that the degeneration in
segmentation performance is mainly attributed to overfitting to source domains
and domain shift. To this end, we present a novel generalizable medical image
segmentation method. To be specific, we design our approach as a multi-task
paradigm by combining the segmentation model with a self-supervision
domain-specific image restoration (DSIR) module for model regularization. We
also design a random amplitude mixup (RAM) module, which incorporates low-level
frequency information of different domain images to synthesize new images. To
guide our model be resistant to domain shift, we introduce a semantic
consistency loss. We demonstrate the performance of our method on two public
generalizable segmentation benchmarks in medical images, which validates our
method could achieve the state-of-the-art performance.

Computer Vision and Pattern Recognition