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

TL;DR

This paper introduces a novel context-aware pseudo-label refinement method for source-free unsupervised domain adaptation in fundus image segmentation, improving pseudo-label quality by leveraging context relations and feature clustering.

Contribution

It proposes a context-similarity learning module and a pseudo-label revision strategy to enhance pseudo-label accuracy without source data, achieving state-of-the-art results.

Findings

State-of-the-art performance on cross-domain fundus image segmentation

Effective pseudo-label refinement using context relations

Robustness to domain gaps in medical imaging

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.