  Medical data often exhibits long-tail distributions with heavy class
imbalance, which naturally leads to difficulty in classifying the minority
classes (i.e., boundary regions or rare objects). Recent work has significantly
improved semi-supervised medical image segmentation in long-tailed scenarios by
equipping them with unsupervised contrastive criteria. However, it remains
unclear how well they will perform in the labeled portion of data where class
distribution is also highly imbalanced. In this work, we present ACTION++, an
improved contrastive learning framework with adaptive anatomical contrast for
semi-supervised medical segmentation. Specifically, we propose an adaptive
supervised contrastive loss, where we first compute the optimal locations of
class centers uniformly distributed on the embedding space (i.e., off-line),
and then perform online contrastive matching training by encouraging different
class features to adaptively match these distinct and uniformly distributed
class centers. Moreover, we argue that blindly adopting a constant temperature
$\tau$ in the contrastive loss on long-tailed medical data is not optimal, and
propose to use a dynamic $\tau$ via a simple cosine schedule to yield better
separation between majority and minority classes. Empirically, we evaluate
ACTION++ on ACDC and LA benchmarks and show that it achieves state-of-the-art
across two semi-supervised settings. Theoretically, we analyze the performance
of adaptive anatomical contrast and confirm its superiority in label
efficiency.
