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.

翻译：心肌梗死（MI）是冠状动脉疾病（CAD）的严重类型，及时检测对预防心肌进行性损伤至关重要。本研究提出一种名为自注意力融合网络（SAF-Net）的新型视图融合模型，用于从多视角超声心动图记录中检测心肌梗死。该框架采用心尖二腔心（A2C）和心尖四腔心（A4C）视角的超声心动图记录进行分类。从两种视角的每条记录中提取三个参考帧，并部署预训练深度网络提取高代表性特征。SAF-Net模型通过自注意力机制学习特征向量间的依赖关系。其紧凑架构包含三个主要部分：用于降维的特征嵌入模块、用于视图池化的自注意力模块以及用于分类的全连接层，因此计算效率较高。基于包含160例患者的HMC-QU-TAU数据集（含A2C与A4C视角超声心动图记录）进行实验评估，SAF-Net模型实现了88.26%的精确率、77.64%的灵敏度和78.13%的准确率。结果表明，SAF-Net模型在多视角超声心动图记录上实现了最准确的心肌梗死检测。