bibtext: @InProceedings{ Bai_CrossPhase_MICCAI2024,
                   author = { Bai, Bizhe and Zhou, Yan-Jie and Hu, Yujian and Mok, Tony C. W. and Xiang, Yilang and Lu, Le and Zhang, Hongkun and Xu, Minfeng },
                   title = { { Cross-Phase Mutual Learning Framework for Pulmonary Embolism Identification on Non-Contrast CT Scans } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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.
title: Cross-Phase Mutual Learning Framework for Pulmonary Embolism Identification on Non-Contrast CT Scans
authors: Bai, Bizhe Zhou, Yan-Jie Hu, Yujian Mok, Tony C. W. Xiang, Yilang Lu, Le Zhang, Hongkun Xu, Minfeng