bibtext: @InProceedings{ Maa_CoReEcho_MICCAI2024,
                   author = { Maani, Fadillah Adamsyah and Saeed, Numan and Matsun, Aleksandr and Yaqub, Mohammad },
                   title = { { CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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/BioMedIA-MBZUAI/CoReEcho.
title: CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis
authors: Maani, Fadillah Adamsyah Saeed, Numan Matsun, Aleksandr Yaqub, Mohammad