# CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis

arXiv: https://arxiv.org/abs/2403.10164 · HTML: https://mlsanity.com/paper/2403.10164/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking](https://mlsanity.com/paper/2303.16727/) (2023)
- [Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography](https://mlsanity.com/paper/1908.06948/) (2019)
- [UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning](https://mlsanity.com/paper/2201.04676/) (2022)
- [Rank-N-Contrast: Learning Continuous Representations for Regression](https://mlsanity.com/paper/2210.01189/) (2022)
- [Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation](https://mlsanity.com/paper/2107.00977/) (2021)
- [Early Detection of Myocardial Infarction in Low-Quality Echocardiography](https://mlsanity.com/paper/2010.02281/) (2020)
- [Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment](https://mlsanity.com/paper/2112.11700/) (2021)
- [EchoCoTr: Estimation of the Left Ventricular Ejection Fraction from Spatiotemporal Echocardiography](https://mlsanity.com/paper/2209.04242/) (2022)
- [Light-weight spatio-temporal graphs for segmentation and ejection fraction prediction in cardiac ultrasound](https://mlsanity.com/paper/2207.02549/) (2022)
- [Ensemble Learning of Myocardial Displacements for Myocardial Infarction Detection in Echocardiography](https://mlsanity.com/paper/2303.06744/) (2023)
- [PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism Diagnosis](https://mlsanity.com/paper/2308.14050/) (2023)

## Built on by (most cited first, 8 of 8)

- [Embodied-R1: Reinforced Embodied Reasoning for General Robotic Manipulation](https://mlsanity.com/paper/2508.13998/) (2025)
- [ProtoEFNet: Dynamic Prototype Learning for Inherently Interpretable Ejection Fraction Estimation in Echocardiography](https://mlsanity.com/paper/2512.03339/) (2025)
- [Q-PART: Quasi-Periodic Adaptive Regression with Test-time Training for Pediatric Left Ventricular Ejection Fraction Regression](https://mlsanity.com/paper/2503.04131/) (2025)
- [Generative Regression for Left Ventricular Ejection Fraction Estimation from Echocardiography Video](https://mlsanity.com/paper/2602.08202/) (2026)
- [Echo-E3Net: Efficient Endocardial Spatio-Temporal Network for Ejection Fraction Estimation](https://mlsanity.com/paper/2503.17543/) (2025)
- [Mamba-3D as Masked Autoencoders for Accurate and Data-Efficient Analysis of Medical Ultrasound Videos](https://mlsanity.com/paper/2503.20258/) (2025)
- [Did the Grid Erase the Event? EndoClock for Auditing Medical World-Model Pipelines](https://mlsanity.com/paper/2608.09266/) (2026)
- [Self-Supervised Cardiac Phase Detection via Single-Parameter Latent Orbits](https://mlsanity.com/paper/2609.11650/) (2026)

## Abstract (arXiv)

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.