解决医学图像分割在无源域适应下的伪标签噪声问题，提升边界精度。
UP2D: Uncertainty-aware Progressive Pseudo-label Denoising for Source-Free Domain Adaptive Medical Image Segmentation
- 基于不确定性引导的渐进式伪标签去噪，逐步优化标签质量。
- 在三个视网膜图像数据集上超越现有方法，边界分割更准确。
- 适合医疗图像领域需要高精度分割的研究者使用。
医学图像分割模型在域偏移下性能显著下降，尤其当无法获取源端图像时更为严重。本文提出一种面向无源域自适应（SFDA）的不确定性感知渐进式伪标签去噪框架（UP2D），以缓解伪标签噪声与类别不平衡问题。UP2D包含三个核心模块：(i) 精炼原型过滤模块，抑制无效区域并构建可靠类别原型，实现伪标签去噪；(ii) 基于不确定性的加权平均教师更新（UG-EMA），根据空间加权边界不确定性选择性更新教师模型；(iii) 分位数熵最小化策略，聚焦于模糊区域学习，避免对简单像素过度自信。该单阶段师生框架逐步提升伪标签质量，降低确认偏差。在三个具有挑战性的视网膜眼底图像基准测试中，UP2D在标准与开放域设置下均达到领先性能，显著优于以往无监督域自适应与无源域自适应方法，且保持优异的边界精度。
原文摘要 · Abstract (English)
Medical image segmentation models face severe performance drops under domain shifts, especially when data sharing constraints prevent access to source images. We present a novel Uncertainty-aware Progressive Pseudo-label Denoising (UP2D) framework for source-free domain adaptation (SFDA), designed to mitigate noisy pseudo-labels and class imbalance during adaptation. UP2D integrates three key components: (i) a Refined Prototype Filtering module that suppresses uninformative regions and constructs reliable class prototypes to denoise pseudo-labels, (ii) an Uncertainty-Guided EMA (UG-EMA) strategy that selectively updates the teacher model based on spatially weighted boundary uncertainty, and (iii) a quantile-based entropy minimization scheme that focuses learning on ambiguous regions while avoiding overconfidence on easy pixels. This single-stage student-teacher framework progressively improves pseudo-label quality and reduces confirmation bias. Extensive experiments on three challenging retinal fundus benchmarks demonstrate that UP2D achieves state-of-the-art performance across both standard and open-domain settings, outperforming prior UDA and SFDA approaches while maintaining superior boundary precision.
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