  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
