TY  - EJOU
AU  - Ye, Zi 
AU  - Kumar, Yogan Jaya 
AU  - Sing, Goh Ong 
AU  - Song, Fengyan 
AU  - Ni, Xianda 

TI  - Classification of Echocardiographic Standard Views Using a Hybrid Attention-based Approach
T2  - Intelligent Automation \& Soft Computing

PY  - 2022
VL  - 33
IS  - 2
SN  - 2326-005X

AB  - The determination of the probe viewpoint forms an essential step in automatic echocardiographic image analysis. However, classifying echocardiograms at the video level is complicated, and previous observations concluded that the most significant challenge lies in distinguishing among the various adjacent views. To this end, we propose an ECHO-Attention architecture consisting of two parts. We first design an ECHO-ACTION block, which efficiently encodes Spatio-temporal features, channel-wise features, and motion features. Then, we can insert this block into existing ResNet architectures, combined with a self-attention module to ensure its task-related focus, to form an effective ECHO-Attention network. The experimental results are confirmed on a dataset of 2693 videos acquired from 267 patients that trained cardiologist has manually labeled. Our methods provide a comparable classification performance (overall accuracy of 94.81%) on the entire video sample and achieved significant improvements on the classification of anatomically similar views (precision 88.65% and 81.70% for parasternal short-axis apical view and parasternal short-axis papillary view on 30-frame clips, respectively).
KW  - Artificial intelligence; attention mechanism; classification; echocardiogram views

DO  - 10.32604/iasc.2022.023555