MReg: A Novel Regression Model with MoE-based Video Feature Mining for Mitral Regurgitation Diagnosis
Journal:
arXiv
Published Date:
Jun 30, 2025
Abstract
Color Doppler echocardiography is a crucial tool for diagnosing mitral
regurgitation (MR). Recent studies have explored intelligent methods for MR
diagnosis to minimize user dependence and improve accuracy. However, these
approaches often fail to align with clinical workflow and may lead to
suboptimal accuracy and interpretability. In this study, we introduce an
automated MR diagnosis model (MReg) developed on the 4-chamber cardiac color
Doppler echocardiography video (A4C-CDV). It follows comprehensive feature
mining strategies to detect MR and assess its severity, considering clinical
realities. Our contribution is threefold. First, we formulate the MR diagnosis
as a regression task to capture the continuity and ordinal relationships
between categories. Second, we design a feature selection and amplification
mechanism to imitate the sonographer's diagnostic logic for accurate MR
grading. Third, inspired by the Mixture-of-Experts concept, we introduce a
feature summary module to extract the category-level features, enhancing the
representational capacity for more accurate grading. We trained and evaluated
our proposed MReg on a large in-house A4C-CDV dataset comprising 1868 cases
with three graded regurgitation labels. Compared to other weakly supervised
video anomaly detection and supervised classification methods, MReg
demonstrated superior performance in MR diagnosis. Our code is available at:
https://github.com/cskdstz/MReg.
