Semantic-aware Temporal Channel-wise Attention for Cardiac Function Assessment

TL;DR

This paper introduces a semantic-aware temporal channel-wise attention model for automatic cardiac function assessment from echocardiogram videos, emphasizing left ventricular motion and segmentation to improve LVEF prediction accuracy.

Contribution

It proposes a novel semi-supervised learning framework with a semantic perception-enhanced TCA module for better motion modeling in cardiac video analysis.

Findings

Achieves state-of-the-art performance on Stanford dataset

Reduces MAE by 0.22 and RMSE by 0.26

Improves R^2 by 1.9%

Abstract

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% $R^2$.