  Pulmonary embolism (PE) is a life-threatening condition where rapid and
accurate diagnosis is imperative yet difficult due to predominantly atypical
symptomatology. Computed tomography pulmonary angiography (CTPA) is
acknowledged as the gold standard imaging tool in clinics, yet it can be
contraindicated for emergency department (ED) patients and represents an
onerous procedure, thus necessitating PE identification through non-contrast CT
(NCT) scans. In this work, we explore the feasibility of applying a
deep-learning approach to NCT scans for PE identification. We propose a novel
Cross-Phase Mutual learNing framework (CPMN) that fosters knowledge transfer
from CTPA to NCT, while concurrently conducting embolism segmentation and
abnormality classification in a multi-task manner. The proposed CPMN leverages
the Inter-Feature Alignment (IFA) strategy that enhances spatial contiguity and
mutual learning between the dual-pathway network, while the Intra-Feature
Discrepancy (IFD) strategy can facilitate precise segmentation of PE against
complex backgrounds for single-pathway networks. For a comprehensive assessment
of the proposed approach, a large-scale dual-phase dataset containing 334 PE
patients and 1,105 normal subjects has been established. Experimental results
demonstrate that CPMN achieves the leading identification performance, which is
95.4\% and 99.6\% in patient-level sensitivity and specificity on NCT scans,
indicating the potential of our approach as an economical, accessible, and
precise tool for PE identification in clinical practice.
