DPP-Net: Difficulty Perception-Processing Heterogeneous Network for Semi-supervised Medical Image Segmentation

In semi-supervised medical image segmentation, the scarcity of labeled data makes models prone to learning bias, causing persistent errors in certain regions and eventual over-fitting, significantly impacting segmentation performance. These problematic regions, termed difficult areas, are inadequately addressed by existing methods. To address this, We propose the Difficulty Perception-Processing Heterogeneous Network (DPP-Net). It guides the model in accurately perceiving and rectifying difficult areas, overcoming learning bias. Specifically, we introduce the Global Mutual Perception (GMP) to establish a comprehensive information perception channel between sample data, enabling a more holistic and accurate perception of difficult areas. The Difficulty-Aware Rectification (DAR) structure ensures continuous monitoring of difficult areas during training, allowing for timely adjustments to errors. Additionally, the Adaptive Competitive Pseudo-Label (ACP) Augmentation strategy enhances pseudo-labels through adaptive confidence competition. Experimental results on two different medical image databases (CT and MRI) demonstrate that our approach outperforms several state-of-the-art methods.