  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.
