Pulmonary Embolism (PE) is a critical medical condition characterized by obstructions in the pulmonary arteries. Despite being a major health concern, it often goes underdiagnosed leading to detrimental clinical outcomes. The increasing reliance on Computed Tomography Pulmonary Angiography for diagnosis presents challenges and a pressing need for enhanced diagnostic solutions. The primary objective of this study is to leverage deep learning techniques to enhance the Computer Assisted Diagnosis of PE. This study presents a comprehensive dual-pronged approach combining classification and detection for PE diagnosis. We introduce an Attention-Guided Convolutional Neural Network (AG-CNN) for classification, addressing both global and local lesion region. For detection, state-of-the-art models are employed to pinpoint potential PE regions. Different ensembling techniques further improve detection accuracy by combining predictions from different models. Finally, a heuristic strategy integrates classifier outputs with detection results, ensuring robust and accurate PE identification. Our attention-guided classification approach, tested on the Ferdowsi University of Mashhad's Pulmonary Embolism (FUMPE) dataset, outperformed the baseline model DenseNet-121 by achieving an 8.1% increase in the Area Under the Receiver Operating Characteristic. By employing ensemble techniques with detection models, the mean average precision (mAP) was considerably enhanced by a 4.7% increase. The classifier-guided framework further refined the mAP and F1 scores over the ensemble models. Our research offers a comprehensive approach to PE diagnostics using deep learning, addressing the prevalent issues of underdiagnosis and misdiagnosis. We aim to improve PE patient care by integrating AI solutions into clinical workflows, highlighting the potential of human-AI collaboration in medical diagnostics.

翻译：肺栓塞是一种以肺动脉阻塞为特征的危急病症。尽管这一疾病对健康构成重大威胁，但由于诊断不足常导致不良临床结局。随着计算机断层扫描肺动脉造影在诊断中的广泛应用，如何提升诊断效能成为亟待解决的挑战。本研究旨在利用深度学习技术增强肺栓塞的计算机辅助诊断能力。我们提出了一种结合分类与检测的双管齐下综合方案：在分类任务中引入注意力引导卷积神经网络，同时关注全局与局部病灶区域；在检测任务中采用先进模型精确定位潜在肺栓塞区域。通过不同集成策略融合多模型预测结果，进一步提升检测精度。最终，基于启发式策略整合分类器输出与检测结果，实现稳健准确的肺栓塞识别。所提出的注意力引导分类方法在马什哈德菲尔多西大学肺栓塞数据集上测试，其受试者工作特征曲线下面积较基线模型DenseNet-121提升8.1%。检测模型采用集成技术后，平均精度均值显著提高4.7%。在集成模型基础上，分类器引导框架进一步优化了平均精度均值与F1分数。本研究提供了基于深度学习的肺栓塞诊断综合方案，有效应对诊断不足与误诊问题。通过将人工智能解决方案融入临床工作流程，旨在改善肺栓塞患者诊疗水平，突显人机协作在医学诊断中的潜力。