精准定位胸片中导管位置，减少误报并量化不确定性。
Uncertainty-Aware Compositional Localization and Placement Assessment of Catheters and Tubes in Chest X-Rays
- 分段检测导管片段，用图聚类整合为完整设备实例。
- 检测率提升26%，假阳性减少75%，尖端定位误差显著降低。
- 适合临床部署，小模型高精度，适合资源受限环境。
评估胸片中导管和管路的位置对安全至关重要，但当前方法繁琐且易出错。现有深度学习方法或仅全局分类位置，丢失具体设备信息；或将所有设备合并为单一掩码，导致重叠时无法逐设备评估。本文提出UCompCXR，一种组合式框架：检测局部导管片段，通过基于图的聚类将其关联为设备实例，利用精度加权高斯估计融合各片段尖端预测，并对每个设备进行位置分类。在包含30,083张图像的RANZCR CLiP数据集上（5折患者级交叉验证，使用自助法置信区间），该模型相比共享MobileNetV3主干的强基线，检测设备数量多26%，假阳性减少75%，尖端不确定性校准良好（95%覆盖率=0.948）。整体尖端误差虽上升，但源于模型发现基线遗漏的设备（尤其是鼻胃管）。在匹配设备上，灾难性定位错误大幅下降。模型仅需227万参数，单次前向传播即可部署于资源受限的临床硬件。
原文摘要 · Abstract (English)
Assessing catheter and tube placement on chest X-rays is safety-critical yet tedious and error-prone. Current deep learning methods either classify placement globally -- losing track of which device is where -- or segment all devices into a single mask, making per-device assessment impossible when catheters overlap. We introduce UCompCXR, a compositional framework that detects local catheter fragments, associates them into device instances via graph-based clustering, fuses per-fragment tip predictions through precision-weighted Gaussian estimation, and classifies placement per device. On the RANZCR CLiP dataset (30,083 images, 5-fold patient-level CV with bootstrap CIs), UCompCXR detects 26% more devices than a strong multi-task baseline sharing the same MobileNetV3 backbone, with 75% fewer false positives and well-calibrated tip uncertainty (95% coverage = 0.948). The aggregate tip error rises -- but only because the model finds devices the baseline misses entirely, especially nasogastric tubes. On matched devices, catastrophic localization failures drop substantially. At 2.27M parameters in a single forward pass, the model is deployable on resource-constrained clinical hardware.
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