SAF-Net: Self-Attention Fusion Network for Myocardial Infarction
  Detection using Multi-View Echocardiography
Ilke Adalioglu Mete Ahishali Aysen Degerli Serkan Kiranyaz Moncef Gabbouj
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.