并非所有像素都平等：面向含噪标签医学分割的像素级元学习
Not All Pixels Are Equal: Pixel-wise Meta-Learning for Medical Segmentation with Noisy Labels
AI总结：
提出MetaDCSeg框架，通过动态学习像素级权重并引入动态中心距离机制建模边界不确定性，抑制噪声标签影响并提升边界分割性能。
摘要：
医学图像分割对于临床应用至关重要，但常常受到噪声标注和模糊解剖边界的干扰，限制了其在现实场景中的应用。现有方法通常直接适应为实例分类设计的噪声标签学习技术，忽视了医学分割中像素级异质性及其空间和解剖上的难度差异。因此，全局假设或简单的置信度指标无法解决这些局部变化，导致边界模糊问题未得到解决。为解决这一问题，我们提出MetaDCSeg，一个鲁棒的框架，动态学习最优像素级权重以抑制噪声标签的影响，同时保留可靠标注。通过动态中心距离（DCD）机制显式建模边界不确定性，我们的方法利用前景、背景和边界中心的加权特征距离，引导模型关注模糊边界附近的难分割像素。该策略能够更精确地处理结构边界（这些边界常被现有方法忽略），并显著提升分割性能。在四个不同噪声水平的基准数据集上的大量实验表明，MetaDCSeg优于现有最先进方法。
英文摘要：
Medical image segmentation is crucial for clinical applications, but it is frequently disrupted by noisy annotations and ambiguous anatomical boundaries, limiting its application in real-world scenarios. Existing methods often directly adapt noisy label learning techniques designed for instance classification, overlooking the pixel-wise heterogeneity in medical segmentation with its spatially and anatomically varying difficulties. Consequently, global assumptions or simple confidence metrics fail to address these local variations, leaving boundary ambiguities unresolved. To address this issue, we propose MetaDCSeg, a robust framework that dynamically learns optimal pixel-wise weights to suppress the influence of noisy labels while preserving reliable annotations. By explicitly modeling boundary uncertainty through a Dynamic Center Distance (DCD) mechanism, our approach utilizes weighted feature distances for foreground, background, and boundary centers, directing the model's attention toward hard-to-segment pixels near ambiguous boundaries. This strategy enables more precise handling of structural boundaries, which are often overlooked by existing methods, and significantly enhances segmentation performance. Extensive experiments across four benchmark datasets with varying noise levels demonstrate that MetaDCSeg outperforms existing state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
