Common Pattern Prior-Driven Semi-Supervised Medical Image Segmentation

Semi-supervised learning (SSL) has emerged as a promising paradigm for medical image segmentation, aiming to alleviate the scarcity of high-quality annotations by combining limited labeled and abundant unlabeled data. However, existing SSL methods suffer from inherent limitations: 1) consistency regularization overly relies on enforcing prediction consistency under different perturbations, neglecting deep exploration of semantic and discriminative features; 2) pseudo-labeling methods are prone to introducing noise, which in turn undermines the stability of model training. To enable high-quality and more stable model learning, we propose a common pattern prior-driven network (CPP-Net) for semi-supervised medical image segmentation. To improve training quality, CPP-Net proposes a pattern learning mechanism that extracts each class's core semantic information for high-quality feature learning. At its core, it is a dynamically updated common pattern bank (CP-Bank), which stores class-specific patterns learned throughout training and serves as high-quality prior knowledge for the model. By reusing CP-Bank patterns, CPP-Net reconstructs current-stage features, reduces redundant learning of shared patterns, and boosts feature robustness and discriminability. Furthermore, an information gain-driven update strategy is proposed to ensure that the CP-Bank is aligned with the historical mean of pattern distributions, preventing excessive bias toward transient local patterns. To enhance training stability, a dynamic regulation function is designed to adaptively modulate the impact of pseudo-labels according to their confidence, thereby mitigating the adverse effects of low-confidence data. Through extensive experiments on various 2D/3D medical image segmentation datasets, CPP-Net demonstrates its effectiveness and generalizability, and achieves 7.5% mean Dice improvement over SOTA.