医学影像中导管定位新方法，精准且能避免致命误判。
Risk-Sensitive Conformal Prediction for Catheter Placement Detection in Chest X-rays
- 多任务学习+风险敏感校准，同时完成分类、分割与关键点检测。
- 关键病灶覆盖率达99.29%，整体覆盖率90.68%，误报率趋近于零。
- 适合临床部署，尤其对高危误判零容忍的医疗场景。
本文提出一种新的胸部X光片中导管与置管位置检测方法，结合多任务学习与风险敏感的分位预测，满足关键临床需求。模型同时执行分类、分割和关键点检测，利用任务间协同提升整体性能。通过风险敏感的分位预测，提供具有统计保障的预测集合，显著提高对临床关键发现的可靠性。实验表明，整体经验覆盖率达90.68%，关键条件覆盖率达99.29%，预测集合精度优异。最重要的是，该方法实现零高风险误判（即系统将危险导管错误判定为正常），极大增强临床可用性。本工作实现了精准预测与可量化不确定性并重，是生命关键医疗应用的重要进展。
原文摘要 · Abstract (English)
This paper presents a novel approach to catheter and line position detection in chest X-rays, combining multi-task learning with risk-sensitive conformal prediction to address critical clinical requirements. Our model simultaneously performs classification, segmentation, and landmark detection, leveraging the synergistic relationship between these tasks to improve overall performance. We further enhance clinical reliability through risk-sensitive conformal prediction, which provides statistically guaranteed prediction sets with higher reliability for clinically critical findings. Experimental results demonstrate excellent performance with 90.68\% overall empirical coverage and 99.29\% coverage for critical conditions, while maintaining remarkable precision in prediction sets. Most importantly, our risk-sensitive approach achieves zero high-risk mispredictions (cases where the system dangerously declares problematic tubes as confidently normal), making the system particularly suitable for clinical deployment. This work offers both accurate predictions and reliably quantified uncertainty -- essential features for life-critical medical applications.
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