  To bridge the gap between the source and target domains in unsupervised
domain adaptation (UDA), the most common strategy puts focus on matching the
marginal distributions in the feature space through adversarial learning.
However, such category-agnostic global alignment lacks of exploiting the
class-level joint distributions, causing the aligned distribution less
discriminative. To address this issue, we propose in this paper a novel margin
preserving self-paced contrastive Learning (MPSCL) model for cross-modal
medical image segmentation. Unlike the conventional construction of contrastive
pairs in contrastive learning, the domain-adaptive category prototypes are
utilized to constitute the positive and negative sample pairs. With the
guidance of progressively refined semantic prototypes, a novel margin
preserving contrastive loss is proposed to boost the discriminability of
embedded representation space. To enhance the supervision for contrastive
learning, more informative pseudo-labels are generated in target domain in a
self-paced way, thus benefiting the category-aware distribution alignment for
UDA. Furthermore, the domain-invariant representations are learned through
joint contrastive learning between the two domains. Extensive experiments on
cross-modal cardiac segmentation tasks demonstrate that MPSCL significantly
improves semantic segmentation performance, and outperforms a wide variety of
state-of-the-art methods by a large margin.
