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% $R^2$.

翻译：心脏功能评估旨在通过超声心动图视频预测左心室射血分数（LVEF），这要求模型关注心脏周期中左心室的变化。如何从超声心动图视频中准确、自动地评估心脏功能是智能辅助医疗领域的一个有价值课题。现有的基于视频的方法未能充分关注左心室区域，也未关注由运动引起的左心室变化。在这项工作中，我们提出了一种半监督辅助学习范式，结合左心室分割任务，有助于左心室区域的表示学习。为了更好地建模运动信息的重要性，我们引入了一个时间通道注意力（TCA）模块来增强描述运动的通道。此外，我们通过将左心室分割图作为输入来重构具有语义感知的TCA模块，以聚焦于左心室的运动模式。最后，为降低直接LVEF回归的难度，我们利用基于锚点的分类与回归方法来预测LVEF。我们的方法在斯坦福数据集上实现了最优性能，MAE提升0.22，RMSE提升0.26，$R^2$提升1.9%。