  Coronary artery disease (CAD) is one of the primary causes leading deaths
worldwide. The presence of atherosclerotic lesions in coronary arteries is the
underlying pathophysiological basis of CAD, and accurate extraction of
individual arterial branches using invasive coronary angiography (ICA) is
crucial for stenosis detection and CAD diagnosis. We propose an innovative
approach called the Edge Attention Graph Matching Network (EAGMN) for coronary
artery semantic labeling. By converting the coronary artery semantic
segmentation task into a graph node similarity comparison task, identifying the
node-to-node correspondence would assign semantic labels for each arterial
branch. More specifically, The EAGMN utilizes the association graph constructed
from the two individual graphs as input. Experimental results indicate the
EAGMN achieved a weighted accuracy of 0.8653, a weighted precision of 0.8656, a
weighted recall of 0.8653 and a weighted F1-score of 0.8643. Furthermore, we
employ ZORRO to provide interpretability and explainability of the graph
matching for artery semantic labeling. These findings highlight the potential
of the EAGMN for accurate and efficient coronary artery semantic labeling using
ICAs. By leveraging the inherent characteristics of ICAs and incorporating
graph matching techniques, our proposed model provides a promising solution for
improving CAD diagnosis and treatment
