Style-Aware Blending and Prototype-Based Cross-Contrast Consistency for Semi-Supervised Medical Image Segmentation

Weak-strong consistency learning strategies are widely employed in semi-supervised medical image segmentation to train models by leveraging limited labeled data and enforcing weak-to-strong consistency. However, most existing methods primarily focus on designing and combining various perturbation schemes, overlooking the intrinsic potential and limitations of the framework itself. In this paper, we identify two critical deficiencies: (1) separated training data streams, which lead to confirmation bias dominated by the labeled stream; and (2) incomplete utilization of supervisory signals, which limits exploration of strong-to-weak consistency. To address these challenges, we propose a style-aware blending and prototype-based crosscontrast consistency learning framework. Specifically, inspired by the empirical observation that the distribution mismatch between labeled and unlabeled data can be characterized by their statistical moments, we design a style-guided distribution blending module to bridge the independent training data streams. Meanwhile, considering the potential noise in strong pseudolabels, we introduce a prototype-based cross-contrast strategy to enable the model to learn informative supervisory signals from both weak-to-strong and strong-to-weak predictions, while mitigating the adverse effects of noise. Extensive experiments demonstrate the effectiveness and superiority of our framework across multiple medical image segmentation benchmarks under various semi-supervised settings. The code is available at https://gndlwch2w.github.io/spc-demo.