用聚类校准钙化体积预测，提升临床风险分诊准确率。
Conformal coronary calcification volume estimation with conditional coverage via histogram clustering
- 基于聚类的条件分位数预测框架，无需重新训练模型
- 3D UNet模型覆盖率达95%以上，分诊效率更优
- 适合需高置信度风险评估的冠心病筛查场景
CT扫描中偶然发现的冠状动脉钙化可促使早期干预，但过度报告可能影响患者健康并加重医疗负担。因此，自动报告钙化评分时需审慎处理。本文提出一种基于聚类的条件分位数预测框架，对已训练的分割网络提供校准后的预测区间，无需重新训练。该方法在3D UNet模型（确定性、MCDropout、深度集成）上进行调优和校准，实现了与传统分位数预测相当的覆盖率（>95%），同时在分诊指标上表现更优。有意义的钙化评分预测区间可依据风险预测置信度实现患者分诊。
原文摘要 · Abstract (English)
Incidental detection and quantification of coronary calcium in CT scans could lead to the early introduction of lifesaving clinical interventions. However, over-reporting could negatively affect patient wellbeing and unnecessarily burden the medical system. Therefore, careful considerations should be taken when automatically reporting coronary calcium scores. A cluster-based conditional conformal prediction framework is proposed to provide score intervals with calibrated coverage from trained segmentation networks without retraining. The proposed method was tuned and used to calibrate predictive intervals for 3D UNet models (deterministic, MCDropout and deep ensemble) reaching similar coverage with better triage metrics compared to conventional conformal prediction. Meaningful predictive intervals of calcium scores could help triage patients according to the confidence of their risk category prediction.
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