  Deep learning (DL) models have been advancing automatic medical image
analysis on various modalities, including echocardiography, by offering a
comprehensive end-to-end training pipeline. This approach enables DL models to
regress ejection fraction (EF) directly from 2D+time echocardiograms, resulting
in superior performance. However, the end-to-end training pipeline makes the
learned representations less explainable. The representations may also fail to
capture the continuous relation among echocardiogram clips, indicating the
existence of spurious correlations, which can negatively affect the
generalization. To mitigate this issue, we propose CoReEcho, a novel training
framework emphasizing continuous representations tailored for direct EF
regression. Our extensive experiments demonstrate that CoReEcho: 1) outperforms
the current state-of-the-art (SOTA) on the largest echocardiography dataset
(EchoNet-Dynamic) with MAE of 3.90 & R2 of 82.44, and 2) provides robust and
generalizable features that transfer more effectively in related downstream
tasks. The code is publicly available at https://github.com/fadamsyah/CoReEcho.
