  Pulmonary embolism (PE) represents a thrombus ("blood clot"), usually
originating from a lower extremity vein, that travels to the blood vessels in
the lung, causing vascular obstruction and in some patients, death. This
disorder is commonly diagnosed using CT pulmonary angiography (CTPA). Deep
learning holds great promise for the computer-aided CTPA diagnosis (CAD) of PE.
However, numerous competing methods for a given task in the deep learning
literature exist, causing great confusion regarding the development of a CAD PE
system. To address this confusion, we present a comprehensive analysis of
competing deep learning methods applicable to PE diagnosis using CTPA at the
both image and exam levels. At the image level, we compare convolutional neural
networks (CNNs) with vision transformers, and contrast self-supervised learning
(SSL) with supervised learning, followed by an evaluation of transfer learning
compared with training from scratch. At the exam level, we focus on comparing
conventional classification (CC) with multiple instance learning (MIL). Our
extensive experiments consistently show: (1) transfer learning consistently
boosts performance despite differences between natural images and CT scans, (2)
transfer learning with SSL surpasses its supervised counterparts; (3) CNNs
outperform vision transformers, which otherwise show satisfactory performance;
and (4) CC is, surprisingly, superior to MIL. Compared with the state of the
art, our optimal approach provides an AUC gain of 0.2\% and 1.05\% for
image-level and exam-level, respectively.
