SAF-Net：基于多视图超声心动图的自我注意力融合网络用于心肌梗死检测
SAF-Net: Self-Attention Fusion Network for Myocardial Infarction Detection using Multi-View Echocardiography
AI总结：
SAF-Net通过自我注意力机制融合多视图超声心动图数据，实现高精度心肌梗死检测。
摘要：
心肌梗死（MI）是冠状动脉疾病（CAD）的一种严重病例，其检测对于防止心肌进一步损伤至关重要。在本研究中，我们提出了一种新的视图融合模型，名为自我注意力融合网络（SAF-Net），用于从多视图超声心动图记录中检测MI。所提出的框架利用心尖两腔（A2C）和心尖四腔（A4C）视图的超声心动图记录进行分类。从每个视图的记录中提取三个参考帧，并部署预训练的深度网络以提取高度代表性的特征。SAF-Net模型利用自我注意力机制来学习提取的特征向量中的依赖关系。所提出的模型由于其紧凑的架构而具有计算效率，该架构有三个主要部分：特征嵌入以减少维度，自我注意力用于视图池化，以及密集层用于分类。实验评估使用HMC-QU-TAU数据集进行，该数据集包含160名患者的心尖两腔和心尖四腔视图超声心动图记录。所提出的SAF-Net模型实现了高水平的性能，精度为88.26%，灵敏度为77.64%，准确率为78.13%。结果表明，SAF-Net模型在多视图超声心动图记录中实现了最准确的MI检测。
英文摘要：
Myocardial infarction (MI) is a severe case of coronary artery disease (CAD) and ultimately, its detection is substantial to prevent progressive damage to the myocardium. In this study, we propose a novel view-fusion model named self-attention fusion network (SAF-Net) to detect MI from multi-view echocardiography recordings. The proposed framework utilizes apical 2-chamber (A2C) and apical 4-chamber (A4C) view echocardiography recordings for classification. Three reference frames are extracted from each recording of both views and deployed pre-trained deep networks to extract highly representative features. The SAF-Net model utilizes a self-attention mechanism to learn dependencies in extracted feature vectors. The proposed model is computationally efficient thanks to its compact architecture having three main parts: a feature embedding to reduce dimensionality, self-attention for view-pooling, and dense layers for the classification. Experimental evaluation is performed using the HMC-QU-TAU dataset which consists of 160 patients with A2C and A4C view echocardiography recordings. The proposed SAF-Net model achieves a high-performance level with 88.26% precision, 77.64% sensitivity, and 78.13% accuracy. The results demonstrate that the SAF-Net model achieves the most accurate MI detection over multi-view echocardiography recordings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
