Representation Alignment with Hybrid Perturbation for Semi-Supervised Medical Image Segmentation

Given the scarcity of labeled data and the high cost of annotation in medical imaging, semi-supervised learning (SSL) has become a promising approach for semantic segmentation tasks. In this paper, we propose RAHP, a representation stabilization framework for semi-supervised medical image segmentation, which addresses perturbation-induced representation inconsistency by integrating hybrid perturbation learning with explicit intermediate representation alignment. Our method adopts a cross-teaching architecture in which two sub-networks guide each other through pseudo-labeling to ensure stable training. To improve consistency across diverse perturbed views, we employ a hybrid perturbation strategy to generate complementary training views. Rather than relying only on prediction-level consistency between these perturbed views, we further introduce a Region-aware Patch Alignment Loss (RPAL) to explicitly align spatially corresponding decoder representations at the patch level. By combining prediction-level supervision with region-aware representation alignment, RAHP constrains not only the output predictions but also the intermediate semantic representations induced by heterogeneous perturbations. Experiments on three public datasets, including ACDC, Synapse, and MMWHS, show that RAHP achieves favorable mean performance compared with the evaluated semi-supervised methods under the reported limited-label settings.