通过自适应替换低置信度区域，提升医学图像分割的精度与稳定性。
CAD: Confidence-Aware Adaptive Displacement for Semi-Supervised Medical Image Segmentation
- 根据置信度动态选择并替换低置信区域，避免扰动破坏分割边界。
- 在多个公开数据集上实现新最优性能，显著提升分割准确率。
- 适合需要高精度且标注稀缺的医学图像分割任务使用。
半监督医学图像分割旨在用极少专家标注实现高质量分割，但一致性学习仍面临挑战。过度扰动会破坏特征对齐，尤其在预测不确定性高的区域。本文提出置信度感知自适应位移（CAD）框架，可选择性识别并用高置信度块替换最大低置信区域。通过在训练过程中动态调整允许的最大替换尺寸与置信度阈值，CAD逐步优化分割质量，不干扰学习过程。在多个公开医学数据集上的实验表明，CAD显著提升分割性能，达到该领域的最新最优水平。代码将在论文发表后开源。
原文摘要 · Abstract (English)
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.
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