Unsupervised Domain Adaptation (UDA) is a learning technique that transfers
knowledge learned in the source domain from labelled training data to the
target domain with only unlabelled data. It is of significant importance to
medical image segmentation because of the usual lack of labelled training data.
Although extensive efforts have been made to optimize UDA techniques to improve
the ac?curacy of segmentation models in the target domain, few studies have
addressed the robustness of these models under UDA. In this study, we propose a
two-stage training strat?egy for robust domain adaptation. In the source
training stage, we utilize adversarial sample augmentation to en?hance the
robustness and generalization capability of the source model. And in the target
training stage, we propose a novel robust pseudo-label and pseudo-boundary
(PLPB) method, which effectively utilizes unlabeled target data to generate
pseudo labels and pseudo boundaries that enable model self-adaptation without
requiring source data. Ex?tensive experimental results on cross-domain fundus
image segmentation confirm the effectiveness and versatility of our method.
Source code of this study is openly accessible at
this https URL

提出了一个两阶段训练策略来实现领域自适应的稳健性，第一阶段利用对抗样本增强来增强源模型的稳健性和泛化能力，第二阶段提出了一种新的稳健伪标签和伪边界方法，有效利用无标签目标数据生成伪标签和伪边界，实现模型的自适应，无需源数据。在跨领域眼底图像分割上的实验结果证实了该方法的有效性和通用性。