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

TL;DR

This paper introduces a novel deep learning approach using attention mechanisms and classifier-guided detection to improve pulmonary embolism diagnosis in CT scans, achieving state-of-the-art accuracy and sensitivity.

Contribution

It presents a new classifier-guided detection method with an attention-based CNN that outperforms existing models in PE detection, especially for small embolisms in peripheral arteries.

Findings

Achieved an AUROC of 0.927 on FUMPE dataset.

Outperformed baseline DenseNet-121 with an 8.1% AUROC gain.

Improved detection metrics with a 3.7% increase in mAP$_{50}$.

Abstract

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.