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The increasing reliance on Computed Tomography Pulmonary Angiography (CTPA)for Pulmonary Embolism (PE) diagnosis presents challenges and a pressing needfor improved diagnostic solutions. The primary objective of this study is toleverage deep learning techniques to enhance the Computer Assisted Diagnosis(CAD) of PE. With this aim, we propose a classifier-guided detection approachthat effectively leverages the classifier's probabilistic inference to directthe detection predictions, marking a novel contribution in the domain ofautomated PE diagnosis. Our classification system includes an Attention-GuidedConvolutional Neural Network (AG-CNN) that uses local context by employing anattention mechanism. This approach emulates a human expert's attention bylooking at both global appearances and local lesion regions before making adecision. The classifier demonstrates robust performance on the FUMPE dataset,achieving an AUROC of 0.927, sensitivity of 0.862, specificity of 0.879, and anF1-score of 0.805 with the Inception-v3 backbone architecture. Moreover, AG-CNNoutperforms 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 techniquesgreatly improves the accuracy of detecting small embolisms in the peripheralarteries. Finally, our proposed classifier-guided detection approach furtherrefines the detection metrics, contributing new state-of-the-art to thecommunity: 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 byintegrating AI solutions into clinical workflows, highlighting the potential ofhuman-AI collaboration in medical diagnostics.

Deep learning in computed tomography pulmonary angiography imaging: a dual-pronged approach for pulmonary embolism detection