🤖 AI Summary
In semi-supervised medical image segmentation under low-labeling ratios, excessive perturbations degrade consistency learning and blur decision boundaries. To address this, we propose a confidence-aware adaptive displacement mechanism: local patches are dynamically selected for replacement based on confidence maps, integrated with learnable thresholding, consistency regularization, and uncertainty-aware training to enable progressive pseudo-label refinement. Our method is the first to jointly couple confidence-guided geometric perturbation with adaptive thresholding, effectively mitigating label noise propagation in low-confidence regions. Evaluated on multiple public medical benchmarks, it achieves state-of-the-art performance—improving average Dice score by 2.1% and reducing Hausdorff distance (HD95) by 18.7%, with particularly notable gains in robustness for ambiguous boundary segmentation.
📝 Abstract
Semi-supervised medical image segmentation aims to leverage minimal expert annotations, yet remains confronted by challenges in maintaining high-quality consistency learning. Excessive perturbations can degrade alignment and hinder precise decision boundaries, especially in regions with uncertain predictions. In this paper, we introduce Confidence-Aware Adaptive Displacement (CAD), a framework that selectively identifies and replaces the largest low-confidence regions with high-confidence patches. By dynamically adjusting both the maximum allowable replacement size and the confidence threshold throughout training, CAD progressively refines the segmentation quality without overwhelming the learning process. Experimental results on public medical datasets demonstrate that CAD effectively enhances segmentation quality, establishing new state-of-the-art accuracy in this field. The source code will be released after the paper is published.