发表机构
Sliced Health（Sliced Health）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
UCompCXR是用于胸部X光片导管与管路评估的组合式框架，可检测装置、关联实例、融合尖端预测并分类放置，在RANZCR CLiP数据集上性能优于基线模型，且参数少可部署于临床硬件。
AI 中文摘要
对胸部X光片中导管与管路的放置情况进行评估是关乎安全的关键环节，但该过程繁琐且易出错。当前的深度学习方法要么对放置情况进行全局分类，从而丢失了各装置的位置信息；要么将所有装置分割为单个掩码，导致导管重叠时无法对单个装置进行评估。我们提出UCompCXR，这是一个组合式框架，可检测导管的局部片段，通过基于图的聚类将这些片段关联为装置实例，通过精度加权高斯估计融合各片段的尖端预测结果，并对每个装置的放置情况进行分类。在RANZCR CLiP数据集（包含30083张图像，采用5折患者级交叉验证并结合自助法置信区间）上，UCompCXR检测到的装置数量比共享同一MobileNetV3骨干网络的强大多任务基线模型多26%，假阳性减少75%，且尖端不确定性校准良好（95%覆盖率为0.948）。总体尖端误差有所上升，但这仅因为该模型发现了基线模型完全遗漏的装置，尤其是鼻胃管。在匹配的装置上，灾难性定位失败大幅减少。该模型仅有227万参数，单次前向传播即可完成推理，可部署在资源受限的临床硬件上。
英文摘要
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.
Comments21 pages, 8 figures Included declaration for paper is under consideration at Pattern Recognition Letters
