新方法让医疗图像分割更可靠，能控制极端风险。
Optimized Certainty Equivalent Risk-Controlling Prediction Sets
- 用置信上界优化预测集参数，确保风险可控
- 在多种风险度量下均达目标满足率，优于旧方法
- 适合医疗等高风险场景，对极端情况更敏感
在医疗图像分割等安全关键应用中，预测系统需提供超越传统期望损失控制的可靠性保障。虽然风险控制预测集（RCPS）能对期望风险提供概率保证，但无法捕捉尾部行为和最坏情形，而这在高风险场景中至关重要。本文提出优化确定等价风险控制预测集（OCE-RCPS），一种新型框架，可对广义优化确定等价（OCE）风险度量（包括条件风险价值（CVaR）和熵风险）提供高概率保证。OCE-RCPS利用上界置信区间识别满足用户指定风险容忍度的预测集参数，并具备可证明的可靠性。理论分析表明，该方法在误覆盖和假阴性率等损失函数下满足所需概率约束。图像分割实验显示，OCE-RCPS在不同风险度量与可靠性配置下均能稳定达到目标满足率，而现有方法OCE-CRC无法提供概率保证。
原文摘要 · Abstract (English)
In safety-critical applications such as medical image segmentation, prediction systems must provide reliability guarantees that extend beyond conventional expected loss control. While risk-controlling prediction sets (RCPS) offer probabilistic guarantees on the expected risk, they fail to capture tail behavior and worst-case scenarios that are crucial in high-stakes settings. This paper introduces optimized certainty equivalent RCPS (OCE-RCPS), a novel framework that provides high-probability guarantees on general optimized certainty equivalent (OCE) risk measures, including conditional value-at-risk (CVaR) and entropic risk. OCE-RCPS leverages upper confidence bounds to identify prediction set parameters that satisfy user-specified risk tolerance levels with provable reliability. We establish theoretical guarantees showing that OCE-RCPS satisfies the desired probabilistic constraint for loss functions such as miscoverage and false negative rate. Experiments on image segmentation demonstrate that OCE-RCPS consistently meets target satisfaction rates across various risk measures and reliability configurations, while OCE-CRC fails to provide probabilistic guarantees.
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