Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning. (arXiv:2201.04812v1 [eess.IV])

Various deep learning models have been developed to segment anatomical
structures from medical images, but they typically have poor performance when
tested on another target domain with different data distribution. Recently,
unsupervised domain adaptation methods have been proposed to alleviate this
so-called domain shift issue, but most of them are designed for scenarios with
relatively small domain shifts and are likely to fail when encountering a large
domain gap. In this paper, we propose DCDA, a novel cross-modality unsupervised
domain …

arxiv collaborative domain adaptation learning segmentation style transfer unsupervised