Medical image segmentation has made significant progress when a large amount
of labeled data are available. However, annotating medical image segmentation
datasets is expensive due to the requirement of professional skills.
Additionally, classes are often unevenly distributed in medical images, which
severely affects the classification performance on minority classes. To address
these problems, this paper proposes Co-Distribution Alignment (Co-DA) for
semi-supervised medical image segmentation. Specifically, Co-DA aligns marginal
predictions on unlabeled data to marginal predictions on labeled data in a
class-wise manner with two differently initialized models before using the
pseudo-labels generated by one model to supervise the other. Besides, we design
an over-expectation cross-entropy loss for filtering the unlabeled pixels to
reduce noise in their pseudo-labels. Quantitative and qualitative experiments
on three public datasets demonstrate that the proposed approach outperforms
existing state-of-the-art semi-supervised medical image segmentation methods on
both the 2D CaDIS dataset and the 3D LGE-MRI and ACDC datasets, achieving an
mIoU of 0.8515 with only 24% labeled data on CaDIS, and a Dice score of 0.8824
and 0.8773 with only 20% data on LGE-MRI and ACDC, respectively.

本文提出了一种基于 Co-Distribution Alignment (Co-DA) 的半监督医学图像分割方法，通过对无标签数据和有标签数据的边际预测进行类别对齐，并设计了过度期望交叉熵损失来减少伪标签中的噪声，实验结果表明该方法在多个公共数据集上优于现有半监督医学图像分割方法。