为CT影像生成可解释的器官级不确定性区间，提升临床可信度。
Conformal Risk Control for Semantic Uncertainty Quantification in Computed Tomography
- 基于长度最小化构建高维共形风险控制方法
- 在真实CT数据上实现高概率覆盖且区间更紧致
- 输出结果与临床解剖特征对齐，适合医生理解
不确定性量化对机器学习预测的信任建立至关重要，尤其在医疗领域。本文提出一种器官依赖的共形风险控制（CRC）方法，确保真实图像以高概率被包含在预测区间内。该方法引入高维共形风险控制框架，结合长度最小化思想，并使过程对每位患者的解剖结构和器官位置具有语义自适应性。所提出的sem-CRC方法在真实世界计算机断层扫描（CT）数据上实现了有效覆盖率，同时提供更紧凑的不确定性区间，并以临床相关特征表达不确定性，便于医生解读和决策。
原文摘要 · Abstract (English)
Uncertainty quantification is necessary for developers, physicians, and regulatory agencies to build trust in machine learning predictors and improve patient care. Beyond measuring uncertainty, it is crucial to express it in clinically meaningful terms that provide actionable insights. This work introduces a conformal risk control (CRC) procedure for organ-dependent uncertainty estimation, ensuring high-probability coverage of the ground-truth image. We first present a high-dimensional CRC procedure that leverages recent ideas of length minimization. We make this procedure semantically adaptive to each patient's anatomy and positioning of organs. Our method, sem-CRC, provides tighter uncertainty intervals with valid coverage on real-world computed tomography (CT) data while communicating uncertainty with clinically relevant features.
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