Guangkun Chen

A deep learning model for the interpretable identification of pulmonary thromboembolism from computed tomography pulmonary angiography

Utilizing the YOLOv11 architecture for automated CTPA analysis yields a highly sensitive and visually interpretable screening mechanism that holds substantial promise for reducing missed diagnoses and accelerating patient triage in acute clinical settings.