Enhancing Pseudo Label Quality for Semi-SupervisedDomain-Generalized Medical Image Segmentation

Generalizing the medical image segmentation algorithms tounseen domains is an important research topic for computer-aided diagnosis and surgery. Most existing methods requirea fully labeled dataset in each source domain. Although (Liuet al. 2021b) developed a semi-supervised domain general-ized method, it still requires the domain labels. This paperpresents a novel confidence-aware cross pseudo supervisionalgorithm for semi-supervised domain generalized medicalimage segmentation. The main goal is to enhance the pseudolabel quality for unlabeled images from unknown distribu-tions. To achieve it, we perform the Fourier transformationto learn low-level statistic information across domains andaugment the images to incorporate cross-domain information.With these augmentations as perturbations, we feed the inputto a confidence-aware cross pseudo supervision network tomeasure the variance of pseudo labels and regularize the net-work to learn with more confident pseudo labels. Our methodsets new records on public datasets,i.e., M&Ms and SCGM.Notably, without using domain labels, our method surpassesthe prior art that even uses domain labels by 11.67% on Diceon M&Ms dataset with 2% labeled data. Code will be avail-able after the conference.