Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation

• URL: http://arxiv.org/abs/2308.07731v1
• Date: Tue, 15 Aug 2023 12:11:33 GMT
• Title: Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation
• Authors: Zheang Huai, Xinpeng Ding, Yi Li, and Xiaomeng Li
• Abstract summary: Source-free unsupervised domain adaptation (SF-UDA) aims at adapting a model trained on the source side to align the target distribution with only the source model and unlabeled target data. We propose a context-aware pseudo-label refinement method for SF-UDA. Experiments on cross-domain fundus images indicate that our approach yields the state-of-the-art results.
• Score: 15.175385504917125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the domain adaptation problem, source data may be unavailable to the target client side due to privacy or intellectual property issues. Source-free unsupervised domain adaptation (SF-UDA) aims at adapting a model trained on the source side to align the target distribution with only the source model and unlabeled target data. The source model usually produces noisy and context-inconsistent pseudo-labels on the target domain, i.e., neighbouring regions that have a similar visual appearance are annotated with different pseudo-labels. This observation motivates us to refine pseudo-labels with context relations. Another observation is that features of the same class tend to form a cluster despite the domain gap, which implies context relations can be readily calculated from feature distances. To this end, we propose a context-aware pseudo-label refinement method for SF-UDA. Specifically, a context-similarity learning module is developed to learn context relations. Next, pseudo-label revision is designed utilizing the learned context relations. Further, we propose calibrating the revised pseudo-labels to compensate for wrong revision caused by inaccurate context relations. Additionally, we adopt a pixel-level and class-level denoising scheme to select reliable pseudo-labels for domain adaptation. Experiments on cross-domain fundus images indicate that our approach yields the state-of-the-art results. Code is available at https://github.com/xmed-lab/CPR.

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