Shih-Hsin Chen , Ho-Chang Kuo , Ken-Pen Weng , Kai-Sheng Hsieh , Ting-Yi Kao , Yi-Hui Chen , Mindy Ming-Huey Guo , Shih-Feng Liu , Chia-Hsuan Liao
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引用次数: 0
Abstract
Object detection in echocardiography is still uncommon, yet precise localization of pediatric valvular regurgitation and Kawasaki-related coronary aneurysms is critical. We introduce two lightweight variants of non-maximum suppression, rNMS-P (used only at inference) and rNMS-TP (used during both training and inference), that improve YOLO v5 to v9 detectors without altering their backbones. The system combines horizontal boxes for valve jets with rotation-aware oriented boxes for coronary segments, applies anatomical constraints by keeping a single high-confidence box per class, and can relax this rule for well-represented lesions. On color and grayscale echocardiograms, rNMS-TP increased [email protected] from 79.7% to 80.5% for regurgitation and from 85.7% to 86.5% for Kawasaki disease, with gains up to 2.3% at the stricter [email protected]:.95 threshold; rNMS-P provided up to a 2.1% boost, all with negligible computational cost, offering a practical path toward explainable, operator-independent cardiac image assessment.
期刊介绍:
Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.