  Generalizing the medical image segmentation algorithms to unseen domains is
an important research topic for computer-aided diagnosis and surgery. Most
existing methods require a fully labeled dataset in each source domain.
Although some researchers developed a semi-supervised domain generalized
method, it still requires the domain labels. This paper presents a novel
confidence-aware cross pseudo supervision algorithm for semi-supervised domain
generalized medical image segmentation. The main goal is to enhance the pseudo
label quality for unlabeled images from unknown distributions. To achieve it,
we perform the Fourier transformation to learn low-level statistic information
across domains and augment the images to incorporate cross-domain information.
With these augmentations as perturbations, we feed the input to a
confidence-aware cross pseudo supervision network to measure the variance of
pseudo labels and regularize the network to learn with more confident pseudo
labels. Our method sets new records on public datasets, i.e., M&Ms and SCGM.
Notably, without using domain labels, our method surpasses the prior art that
even uses domain labels by 11.67% on Dice on M&Ms dataset with 2% labeled data.
Code is available at https://github.com/XMed-Lab/EPL_SemiDG.
