Spatio-Temporal Contrast Network for Data-Efficient Learning of Coronary Artery Disease in Coronary CT Angiography

Coronary artery disease (CAD) poses a significant challenge to cardiovascular patients worldwide, underscoring the crucial role of automated CAD diagnostic technology in clinical settings. Previous methods for diagnosing CAD using coronary artery CT angiography (CCTA) images have certain limitations in widespread replication and clinical application due to the high demand for annotated medical imaging data. In this work, we introduce the Spatio-temporal Contrast Network (SC-Net) for the first time, designed to tackle the challenges of data-efficient learning in CAD diagnosis based on CCTA. SC-Net utilizes data augmentation to facilitate clinical feature learning and leverages spatio-temporal prediction-contrast based on dual tasks to maximize the effectiveness of limited data, thus providing clinically reliable predictive results. Experimental findings from a dataset comprising 218 CCTA images from diverse patients demonstrate that SC-Net achieves outstanding performance in automated CAD diagnosis with a reduced number of training samples. The introduction of SC-Net presents a practical data-efficient learning strategy, thereby facilitating the implementation and application of automated CAD diagnosis across a broader spectrum of clinical scenarios. The source code is publicly available at the following link ( https://github.com/PerceptionComputingLab/SC-Net ).