This paper studies source-free domain adaptive fundus image segmentation
which aims to adapt a pretrained fundus segmentation model to a target domain
using unlabeled images. This is a challenging task because it is highly risky
to adapt a model only using unlabeled data. Most existing methods tackle this
task mainly by designing techniques to carefully generate pseudo labels from
the model's predictions and use the pseudo labels to train the model. While
often obtaining positive adaption effects, these methods suffer from two major
issues. First, they tend to be fairly unstable - incorrect pseudo labels
abruptly emerged may cause a catastrophic impact on the model. Second, they
fail to consider the severe class imbalance of fundus images where the
foreground (e.g., cup) region is usually very small. This paper aims to address
these two issues by proposing the Class-Balanced Mean Teacher (CBMT) model.
CBMT addresses the unstable issue by proposing a weak-strong augmented mean
teacher learning scheme where only the teacher model generates pseudo labels
from weakly augmented images to train a student model that takes strongly
augmented images as input. The teacher is updated as the moving average of the
instantly trained student, which could be noisy. This prevents the teacher
model from being abruptly impacted by incorrect pseudo-labels. For the class
imbalance issue, CBMT proposes a novel loss calibration approach to highlight
foreground classes according to global statistics. Experiments show that CBMT
well addresses these two issues and outperforms existing methods on multiple
benchmarks.

本研究主要研究基于无源域的玻璃体图像分割，旨在通过使用无标签图像将预训练的玻璃体分割模型适应到目标领域。研究提出了一种名为 Class-Balanced Mean Teacher (CBMT) 的模型，旨在解决伪标签不稳定和类别不平衡的问题，并通过实验证明 CBMT 在多个基准测试中优于现有方法。