用深度集成分析多发性硬化脑部病灶分割的不确定性，发现其与病灶大小、形状和皮层受累密切相关。
Explaining Uncertainty in Multiple Sclerosis Cortical Lesion Segmentation Beyond Prediction Errors
- 采用深度集成方法分析病灶级预测不确定性
- 不确定性与病灶大小、形状、皮层受累显著相关
- 适用于临床可解释性研究，适合医学AI开发者参考
可信人工智能在医疗领域至关重要，尤其在高风险任务如医学图像分割中。可解释AI与不确定性量化能显著提升AI的可靠性，涵盖鲁棒性、可用性和可解释性等关键属性。尽管医学影像中的不确定性量化技术已有长足进展，但对其临床信息价值和可解释性的理解仍有限。本研究提出一种可解释性框架，利用深度集成分析多发性硬化患者脑皮层病灶分割中的病灶级预测不确定性。分析重点从不确定性与误差的关系转向临床与工程层面的相关因素。研究发现，实例级不确定性与病灶大小、形状及皮层受累程度强相关。专家标注者反馈也证实，相似因素会影响人工标注信心。在两个数据集（206名患者，近2000个病灶）上进行的评估，涵盖同分布与分布外两种场景，验证了该框架在不同条件下的实用性。
原文摘要 · Abstract (English)
Trustworthy artificial intelligence (AI) is essential in healthcare, particularly for high-stakes tasks like medical image segmentation. Explainable AI and uncertainty quantification significantly enhance AI reliability by addressing key attributes such as robustness, usability, and explainability. Despite extensive technical advances in uncertainty quantification for medical imaging, understanding the clinical informativeness and interpretability of uncertainty remains limited. This study presents an interpretability framework for analyzing lesion-scale predictive uncertainty in cortical lesion segmentation in multiple sclerosis using deep ensembles. The analysis shifts the focus from the uncertainty--error relationship towards clinically relevant medical and engineering factors. Our findings reveal that instance-wise uncertainty is strongly related to lesion size, shape, and cortical involvement. Expert rater feedback confirms that similar factors impede annotator confidence. Evaluations conducted on two datasets (206 patients, almost 2000 lesions) under both in-domain and distribution-shift conditions highlight the utility of the framework in different scenarios.
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