Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive
  Fundus Image Segmentation
Zheang Huai Xinpeng Ding Yi Li Xiaomeng Li
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.