标题： 超声分割中的标签不确定性 显示英文标题

标题： Label Uncertainty for Ultrasound Segmentation

摘要： 在医学影像中，放射科医生之间的观察者变异性常常引入标签不确定性，尤其是在视觉解释具有主观性的模态中。肺部超声（LUS）是一个典型的例子——它经常呈现出高度模糊区域和明显可区分结构的混合，即使对于经验丰富的临床医生来说，一致的标注也具有挑战性。在这项工作中，我们介绍了一种新颖的方法，使用专家提供的每个像素的置信度值来进行标注和训练人工智能模型。我们设计了一种数据标注协议，捕捉放射科医生对每个标记区域的信心，建模现实世界临床数据中固有的随机不确定性。我们证明，在训练过程中结合这些置信度值可以提高分割性能。更重要的是，我们表明这种增强的分割质量可以转化为下游临床上关键任务的更好表现——具体而言，是估计S/F氧合比值、分类S/F比值变化以及预测30天内患者再入院。虽然我们对许多方法进行了实证评估以将不确定性暴露给学习模型，但我们发现一种简单的策略，即使用60%的置信度阈值获得二值化标签来训练模型效果很好。重要的是，高阈值远优于50%阈值的简单方法，这表明在非常自信的像素上进行训练要有效得多。我们的研究系统地调查了使用不同置信度阈值训练的影响，不仅比较了分割指标，还比较了下游临床结果。这些结果表明，标签置信度是一个有价值的信号，当正确利用时，可以显著提高医学影像中人工智能的可靠性和临床实用性。 显示英文摘要

摘要： In medical imaging, inter-observer variability among radiologists often introduces label uncertainty, particularly in modalities where visual interpretation is subjective. Lung ultrasound (LUS) is a prime example-it frequently presents a mixture of highly ambiguous regions and clearly discernible structures, making consistent annotation challenging even for experienced clinicians. In this work, we introduce a novel approach to both labeling and training AI models using expert-supplied, per-pixel confidence values. Rather than treating annotations as absolute ground truth, we design a data annotation protocol that captures the confidence that radiologists have in each labeled region, modeling the inherent aleatoric uncertainty present in real-world clinical data. We demonstrate that incorporating these confidence values during training leads to improved segmentation performance. More importantly, we show that this enhanced segmentation quality translates into better performance on downstream clinically-critical tasks-specifically, estimating S/F oxygenation ratio values, classifying S/F ratio change, and predicting 30-day patient readmission. While we empirically evaluate many methods for exposing the uncertainty to the learning model, we find that a simple approach that trains a model on binarized labels obtained with a (60%) confidence threshold works well. Importantly, high thresholds work far better than a naive approach of a 50% threshold, indicating that training on very confident pixels is far more effective. Our study systematically investigates the impact of training with varying confidence thresholds, comparing not only segmentation metrics but also downstream clinical outcomes. These results suggest that label confidence is a valuable signal that, when properly leveraged, can significantly enhance the reliability and clinical utility of AI in medical imaging.