基于原则的可解释不确定性监督在医学图像分割中
Principle-Guided Supervision for Interpretable Uncertainty in Medical Image Segmentation
AI总结：
本文提出PriUS框架，通过原则指导提升医学图像分割中不确定性的可解释性，实验表明其在保持分割性能的同时提升了不确定性估计的一致性。
摘要：
不确定性量化通过表征模型预测的可靠性，对高风险决策如医学图像分割至关重要。然而，现有方法通常将不确定性简化为标量置信度估计，导致其空间分布语义上受限。本文聚焦于不确定性可解释性，即估计的不确定性是否以人类可理解的方式反映模糊源。我们识别出三个与感知对齐的原则，要求不确定性空间分布反映结构对比度、图像损坏严重程度和解剖结构几何复杂性。基于证据学习，我们开发了原则指导的不确定性监督框架PriUS，在训练过程中显式执行相应的监督目标。我们进一步引入定量指标来衡量预测不确定性与引起模糊性的图像属性之间的一致性。在ACDC、ISIC和WHS数据集上的实验表明，与最先进的方法相比，PriUS在保持竞争性分割性能的同时，产生了更加一致的不确定性估计。
英文摘要：
Uncertainty quantification complements model predictions by characterizing their reliability, which is essential for high-stakes decision making such as medical image segmentation. However, most existing methods reduce uncertainty to a scalar confidence estimate, leaving its spatial distribution semantically underconstrained. In this work, we focus on uncertainty interpretability, namely, whether estimated uncertainty behaves in a human-understandable manner with respect to sources of ambiguity. We identify three perception-aligned principles requiring the spatial distribution of uncertainty to reflect: (1) image contrast between structures, (2) severity of image corruption, and (3) geometric complexity in anatomical structures. Accordingly, we develop a principle-guided uncertainty supervision framework (PriUS) based on evidential learning, in which the corresponding supervision objectives are explicitly enforced during training. We further introduce quantitative metrics to measure the consistency between predicted uncertainty and image attributes that induce ambiguity. Experiments on ACDC, ISIC, and WHS datasets showed that, compared with state-of-the-art methods, PriUS produced more consistent uncertainty estimates while maintaining competitive segmentation performance.
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