Although unsupervised domain adaptation (UDA) is a promising direction to alleviate domain shift, they fall short of their supervised counterparts. In this work, we investigate relatively less explored semi-supervised domain adaptation (SSDA) for medical image segmentation, where access to a few labeled target samples can improve the adaptation performance substantially. Specifically, we propose a two-stage training process. First, an encoder is pre-trained in a self-learning paradigm using a novel domain-content disentangled contrastive learning (CL) along with a pixel-level feature consistency constraint. The proposed CL enforces the encoder to learn discriminative content-specific but domain-invariant semantics on a global scale from the source and target images, whereas consistency regularization enforces the mining of local pixel-level information by maintaining spatial sensitivity. This pre-trained encoder, along with a decoder, is further fine-tuned for the downstream task, (i.e. pixel-level segmentation) using a semi-supervised setting. Furthermore, we experimentally validate that our proposed method can easily be extended for UDA settings, adding to the superiority of the proposed strategy. Upon evaluation on two domain adaptive image segmentation tasks, our proposed method outperforms the SoTA methods, both in SSDA and UDA settings. Code is available at https://github.com/hritam-98/GFDA-disentangled

翻译：尽管无监督域自适应（UDA）是缓解域偏移的有前景方向，但其性能仍不及监督方法。本研究探索了医学图像分割中相对少有人涉足的半监督域自适应（SSDA），通过引入少量标注的目标样本可显著提升自适应性能。具体而言，我们提出两阶段训练流程：首先，采用新颖的域-内容解耦对比学习（CL）结合像素级特征一致性约束，以自学习范式预训练编码器。所提出的对比学习强制编码器从源域和目标域图像中学习全局尺度上具有判别性的内容特定且域不变的语义特征，而一致性正则化则通过保持空间敏感度来挖掘局部像素级信息。该预训练编码器与解码器随后在半监督设置下针对下游任务（即像素级分割）进行微调。此外，实验验证表明，我们提出的方法可轻松扩展至无监督域自适应设置，进一步凸显了其优越性。在两个域自适应图像分割任务上的评估显示，所提方法在SSDA和UDA设置下均优于现有最优方法。代码已开源：https://github.com/hritam-98/GFDA-disentangled