Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays

胸部X光片中导管放置检测的风险敏感共形预测

Risk-Sensitive Conformal Prediction for Catheter Placement Detection in Chest X-rays

本文提出结合多任务学习与风险敏感共形预测的胸部X光导管检测方法，在保证高覆盖率和精确度的同时实现零高风险误预测，为生命攸关医疗应用提供可靠量化不确定性。

胸部X光片中导管与管路的不确定性感知组合式定位及放置评估

Uncertainty-Aware Compositional Localization and Placement Assessment of Catheters and Tubes in Chest X-Rays

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。

Automatic classification of multiple catheters in neonatal radiographs with deep learning

We develop and evaluate a deep learning algorithm to classify multiple catheters on neonatal chest and abdominal radiographs. A convolutional neural network (CNN) was trained using a dataset of 777 neonatal chest and abdominal radiographs, with a split of 81%-9%-10% for training-validation-testing, respectively. We employed ResNet-50 (a CNN), pre-trained on ImageNet. Ground truth labelling was limited to tagging each image to indicate the presence or absence of endotracheal tubes (ETTs), nasogastric tubes (NGTs), and umbilical arterial and venous catheters (UACs, UVCs). The data set included 5…

用于胸部X光片的导管自底向上实例分割

Bottom-Up Instance Segmentation of Catheters for Chest X-Rays

针对胸部X光片中多导管重叠导致个体难以区分的问题，提出基于关联嵌入的自底向上深度学习实例分割方法，以有效处理细长导管的交叉。

Tubular Shape Aware Data Generation for Semantic Segmentation in Medical Imaging

Chest X-ray is one of the most widespread examinations of the human body. In interventional radiology, its use is frequently associated with the need to visualize various tube-like objects, such as puncture needles, guiding sheaths, wires, and catheters. Detection and precise localization of these tube-like objects in the X-ray images is, therefore, of utmost value, catalyzing the development of accurate target-specific segmentation algorithms. Similar to the other medical imaging tasks, the manual pixel-wise annotation of the tubes is a resource-consuming process. In this work, we aim to alle…

胸部X射线设备推理的临床知识图谱

Clinical Knowledge Graphs for Chest X-Ray Device Reasoning

本研究提出一种不确定性感知的临床知识图谱，将胸部X射线设备实例、尖端估计、放置评估及来源信息等作为关联证据，在RANZCR CLiP大规模测试集上验证，实现了保留完整证据的AI推理基础。