Q-PART: Quasi-Periodic Adaptive Regression with Test-time Training for Pediatric Left Ventricular Ejection Fraction Regression
Journal:
arXiv
Published Date:
Mar 6, 2025
Abstract
In this work, we address the challenge of adaptive pediatric Left Ventricular
Ejection Fraction (LVEF) assessment. While Test-time Training (TTT) approaches
show promise for this task, they suffer from two significant limitations.
Existing TTT works are primarily designed for classification tasks rather than
continuous value regression, and they lack mechanisms to handle the
quasi-periodic nature of cardiac signals. To tackle these issues, we propose a
novel \textbf{Q}uasi-\textbf{P}eriodic \textbf{A}daptive \textbf{R}egression
with \textbf{T}est-time Training (Q-PART) framework. In the training stage, the
proposed Quasi-Period Network decomposes the echocardiogram into periodic and
aperiodic components within latent space by combining parameterized helix
trajectories with Neural Controlled Differential Equations. During inference,
our framework further employs a variance minimization strategy across image
augmentations that simulate common quality issues in echocardiogram
acquisition, along with differential adaptation rates for periodic and
aperiodic components. Theoretical analysis is provided to demonstrate that our
variance minimization objective effectively bounds the regression error under
mild conditions. Furthermore, extensive experiments across three pediatric age
groups demonstrate that Q-PART not only significantly outperforms existing
approaches in pediatric LVEF prediction, but also exhibits strong clinical
screening capability with high mAUROC scores (up to 0.9747) and maintains
gender-fair performance across all metrics, validating its robustness and
practical utility in pediatric echocardiography analysis.
