CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis
This work addresses the need for more interpretable and generalizable models in medical image analysis for echocardiography, though it appears incremental as it builds on existing end-to-end training methods.
The paper tackled the problem of limited explainability and generalization in deep learning models for direct ejection fraction regression from 2D+time echocardiograms by proposing CoReEcho, a training framework emphasizing continuous representations, which achieved state-of-the-art performance with MAE of 3.90 and R2 of 82.44 on the EchoNet-Dynamic dataset.
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.