The increasing reliance on Computed Tomography Pulmonary Angiography (CTPA) for Pulmonary Embolism (PE) diagnosis presents challenges and a pressing need for improved diagnostic solutions. The primary objective of this study is to leverage deep learning techniques to enhance the Computer Assisted Diagnosis (CAD) of PE. With this aim, we propose a classifier-guided detection approach that effectively leverages the classifier's probabilistic inference to direct the detection predictions, marking a novel contribution in the domain of automated PE diagnosis. Our classification system includes an Attention-Guided Convolutional Neural Network (AG-CNN) that uses local context by employing an attention mechanism. This approach emulates a human expert's attention by looking at both global appearances and local lesion regions before making a decision. The classifier demonstrates robust performance on the FUMPE dataset, achieving an AUROC of 0.927, sensitivity of 0.862, specificity of 0.879, and an F1-score of 0.805 with the Inception-v3 backbone architecture. Moreover, AG-CNN outperforms the baseline DenseNet-121 model, achieving an 8.1% AUROC gain. While previous research has mostly focused on finding PE in the main arteries, our use of cutting-edge object detection models and ensembling techniques greatly improves the accuracy of detecting small embolisms in the peripheral arteries. Finally, our proposed classifier-guided detection approach further refines the detection metrics, contributing new state-of-the-art to the community: mAP$_{50}$, sensitivity, and F1-score of 0.846, 0.901, and 0.779, respectively, outperforming the former benchmark with a significant 3.7% improvement in mAP$_{50}$. Our research aims to elevate PE patient care by integrating AI solutions into clinical workflows, highlighting the potential of human-AI collaboration in medical diagnostics.

翻译：随着计算机断层扫描肺动脉造影（CTPA）在肺栓塞（PE）诊断中的日益依赖，临床面临挑战并迫切需要更优的诊断方案。本研究的主要目标是利用深度学习技术提升肺栓塞的计算机辅助诊断（CAD）效能。为此，我们提出了一种分类器引导的检测方法，该方法通过有效利用分类器的概率推理来引导检测预测，这是自动化肺栓塞诊断领域的一项创新贡献。我们的分类系统包含一个注意力引导卷积神经网络（AG-CNN），它通过注意力机制利用局部上下文信息。该方法模拟人类专家的注意力模式，在做出决策前同时观察全局外观和局部病灶区域。该分类器在FUMPE数据集上展现出稳健性能，以Inception-v3为骨干架构时，AUROC达到0.927，敏感性为0.862，特异性为0.879，F1分数为0.805。此外，AG-CNN较基线DenseNet-121模型实现了8.1%的AUROC提升。以往研究主要聚焦于主肺动脉中的栓塞检测，而我们采用前沿目标检测模型与集成技术，显著提高了对外周动脉微小栓塞的检测精度。最终，我们提出的分类器引导检测方法进一步优化了检测指标，为该领域贡献了新的最优结果：mAP$_{50}$、敏感性和F1分数分别达到0.846、0.901和0.779，mAP$_{50}$较先前基准提升了3.7%。本研究通过将人工智能解决方案整合至临床工作流程，旨在提升肺栓塞患者护理水平，突显了人机协作在医学诊断中的巨大潜力。