Semantic-aware Temporal Channel-wise Attention for Cardiac Function
  Assessment
Guanqi Chen Guanbin Li
Cardiac function assessment aims at predicting left ventricular ejection
fraction (LVEF) given an echocardiogram video, which requests models to focus
on the changes in the left ventricle during the cardiac cycle. How to assess
cardiac function accurately and automatically from an echocardiogram video is a
valuable topic in intelligent assisted healthcare. Existing video-based methods
do not pay much attention to the left ventricular region, nor the left
ventricular changes caused by motion. In this work, we propose a
semi-supervised auxiliary learning paradigm with a left ventricular
segmentation task, which contributes to the representation learning for the
left ventricular region. To better model the importance of motion information,
we introduce a temporal channel-wise attention (TCA) module to excite those
channels used to describe motion. Furthermore, we reform the TCA module with
semantic perception by taking the segmentation map of the left ventricle as
input to focus on the motion patterns of the left ventricle. Finally, to reduce
the difficulty of direct LVEF regression, we utilize an anchor-based
classification and regression method to predict LVEF. Our approach achieves
state-of-the-art performance on the Stanford dataset with an improvement of
0.22 MAE, 0.26 RMSE, and 1.9% $R^2$.