Domain shift happens in cross-domain scenarios commonly because of the wide
gaps between different domains: when applying a deep learning model
well-trained in one domain to another target domain, the model usually performs
poorly. To tackle this problem, unsupervised domain adaptation (UDA) techniques
are proposed to bridge the gap between different domains, for the purpose of
improving model performance without annotation in the target domain.
Particularly, UDA has a great value for multimodal medical image analysis,
where annotation difficulty is a practical concern. However, most existing UDA
methods can only achieve satisfactory improvements in one adaptation direction
(e.g., MRI to CT), but often perform poorly in the other (CT to MRI), limiting
their practical usage. In this paper, we propose a bidirectional UDA (BiUDA)
framework based on disentangled representation learning for equally competent
two-way UDA performances. This framework employs a unified domain-aware pattern
encoder which not only can adaptively encode images in different domains
through a domain controller, but also improve model efficiency by eliminating
redundant parameters. Furthermore, to avoid distortion of contents and patterns
of input images during the adaptation process, a content-pattern consistency
loss is introduced. Additionally, for better UDA segmentation performance, a
label consistency strategy is proposed to provide extra supervision by
recomposing target-domain-styled images and corresponding source-domain
annotations. Comparison experiments and ablation studies conducted on two
public datasets demonstrate the superiority of our BiUDA framework to current
state-of-the-art UDA methods and the effectiveness of its novel designs. By
successfully addressing two-way adaptations, our BiUDA framework offers a
flexible solution of UDA techniques to the real-world scenario.

本文介绍了一种基于 “分离表达学习” 的双向无监督领域自适应（BiUDA）框架，来提高医疗图像分析中深度学习模型在不同领域间迁移性能，实验结果显示该框架优于当前先进的方法。