14 citations · 26 across the 7 of their papers we have counts for
7 papers
Detecting low left ventricular ejection fraction from ECG using an interpretable and scalable predictor-driven framework
Ya Zhou, Tianxiang Hao, Ziyi Cai +5
Low left ventricular ejection fraction (LEF) frequently remains undetected until progression to symptomatic heart failure, underscoring the need for scalable screening strategies.…
Detecting Structural Heart Disease from Electrocardiograms via a Generalized Additive Model of Interpretable Foundation-Model Predictors
Ya Zhou, Zhaohong Sun, Tianxiang Hao +1
Structural heart disease (SHD) is a prevalent condition with many undiagnosed cases, and early detection is often limited by the high cost and accessibility constraints of echocard…
Bridging Performance Gaps for ECG Foundation Models: A Post-Training Strategy
Ya Zhou, Yujie Yang, Xiaohan Fan +1
ECG foundation models are increasingly popular due to their adaptability across various tasks. However, their clinical applicability is often limited by performance gaps compared t…
Multi-scale Masked Autoencoder for Electrocardiogram Anomaly Detection
Ya Zhou, Yujie Yang, Jianhuang Gan +3
Electrocardiogram (ECG) analysis is a fundamental tool for diagnosing cardiovascular conditions, yet anomaly detection in ECG signals remains challenging due to their inherent comp…
Masked Transformer for Electrocardiogram Classification
Ya Zhou, Xiaolin Diao, Yanni Huo +3
Electrocardiogram (ECG) is one of the most important diagnostic tools in clinical applications. With the advent of advanced algorithms, various deep learning models have been adopt…
CP Degeneracy in Tensor Regression
Ya Zhou, Raymond K. W. Wong, Kejun He
Tensor linear regression is an important and useful tool for analyzing tensor data. To deal with high dimensionality, CANDECOMP/PARAFAC (CP) low-rank constraints are often imposed…
Broadcasted Nonparametric Tensor Regression
Ya Zhou, Raymond K. W. Wong, Kejun He
We propose a novel use of a broadcasting operation, which distributes univariate functions to all entries of the tensor covariate, to model the nonlinearity in tensor regression no…