Generative Regression for Left Ventricular Ejection Fraction Estimation from Echocardiography Video
Jinrong Lv, Xun Gong, Zhaohuan Li, and Weili Jiang

TL;DR
This paper introduces a probabilistic generative model for estimating the distribution of left ventricular ejection fraction from echocardiograms, addressing limitations of traditional regression methods by capturing multimodal uncertainties and improving interpretability.
Contribution
The paper proposes the Multimodal Conditional Score-based Diffusion model for Regression (MCSDR), a novel probabilistic framework that models the posterior distribution of LVEF conditioned on echocardiogram videos and priors.
Findings
Achieves state-of-the-art performance on multiple datasets.
Effectively models multimodal posterior distributions.
Provides interpretable generation trajectories in noisy or variable cases.
Abstract
Estimating Left Ventricular Ejection Fraction (LVEF) from echocardiograms constitutes an ill-posed inverse problem. Inherent noise, artifacts, and limited viewing angles introduce ambiguity, where a single video sequence may map not to a unique ground truth, but rather to a distribution of plausible physiological values. Prevailing deep learning approaches typically formulate this task as a standard regression problem that minimizes the Mean Squared Error (MSE). However, this paradigm compels the model to learn the conditional expectation, which may yield misleading predictions when the underlying posterior distribution is multimodal or heavy-tailed -- a common phenomenon in pathological scenarios. In this paper, we investigate the paradigm shift from deterministic regression toward generative regression. We propose the Multimodal Conditional Score-based Diffusion model for Regression…
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Taxonomy
TopicsCardiovascular Function and Risk Factors · Heart Failure Treatment and Management · Congenital Heart Disease Studies
