Robust Source-Free Domain Adaptation for Fundus Image Segmentation
Lingrui Li Yanfeng Zhou Ge Yang
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 accuracy 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 strategy for robust domain adaptation. In the source
training stage, we utilize adversarial sample augmentation to enhance 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. Extensive 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
https://github.com/LinGrayy/PLPB.