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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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9 results for “echocardiography”

Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factorsstat.ML

Machine learning improves CHD screening accuracy from 70% to 87.7%.

problem Predicting coronary heart disease using echocardiography and clinical features.

method Ensemble machine learning approach with model stacking and two-step stacking.

result Improved CHD classification accuracy from 70% to 87.7%.

machine learning echocardiography coronary heart disease risk factors model stacking

2021-05-20abs ↗pdf ↗

A Study into Echocardiography View Conversioneess.IV

Study converts echocardiography views using adversarial models.

problem Limited information sharing among echocardiography views.

method Adversarial generative models trained with LSGAN, PatchGAN, and cGAN.

result Generated views correlate with real views in LV area and length.

echocardiography view conversion adversarial models generative adversarial networks (GANs)left ventricle (LV)

2019-12-05abs ↗pdf ↗

On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra-observer Variability in 2D Echocardiography Quality Assessmentcs.LG

Paper proposes a method to estimate intra-observer variability in echocardiography quality assessment.

problem Intra-observer variability in echocardiography quality assessment impacts deep neural network reliability.

method Modeling intra-observer variability as aleatoric uncertainty in a regression problem.

result The proposed method reduces error from 0.11 to 0.09, improving test accuracy by 5.7%.

echocardiography deep learning intra-observer variability uncertainty modeling quality assessment

2019-11-02abs ↗pdf ↗

GAN-enhanced Conditional Echocardiogram Generationeess.IV

GANs improve echo frame generation with labeled patches.

problem Automated echocardiography with limited labels.

method Conditional GAN with patch-based discriminator.

result GAN-enhanced echo frames match given segmentation masks.

GAN echocardiography semi-supervised cardiac segmentation machine learning

2019-11-05abs ↗pdf ↗

Dimension-reduced outcome-weighted learning for estimating individualized treatment regimes in observational studiesstat.ML

Proposes a method to estimate personalized treatments from high-dimensional data.

problem Estimating individualized treatment regimes (ITRs) from high-dimensional covariates.

method Directly targets the contrast between potential outcomes, using dimension-reduced outcome-weighted learning.

result Achieves universal consistency, converging to the Bayes risk under mild conditions.

dimension reduction outcome-weighted learning individualized treatment regimes observational studies sepsis

2026-01-11abs ↗pdf ↗

Uncertainty-aware classification and triage of structural heart disease using electrocardiography and echocardiography metricsq-bio.QM

Bayesian neural networks improve SHD classification and uncertainty quantification.

problem Improving screening for structural heart disease using noninvasive ECG and echocardiography.

method Comparing frequentist and Bayesian neural network classifiers on the EchoNext dataset.

result Bayesian classifiers provide more robust uncertainty quantification.

Bayesian inference structural heart disease machine learning electrocardiography echocardiography

2026-05-21abs ↗pdf ↗

A Large-scale Multimodal Study for Predicting Mortality Risk Using Minimal and Low Parameter Models and Separable Risk Assessmentcs.LG

The majority of biomedical studies use limited datasets that may not generalize over large heterogeneous datasets that have been collected over several decades. The current paper develops and validates several multimodal models that can predict 1-year mortality based on a massive clinical dataset. Our focus on predicti…

2019-01-23abs ↗pdf ↗

Detecting Structural Heart Disease from Electrocardiograms via a Generalized Additive Model of Interpretable Foundation-Model Predictorsstat.AP

AI detects heart disease from ECGs with improved interpretability and performance.

problem Undiagnosed structural heart disease due to high cost and accessibility of echocardiography.

method Generalized additive model integrating clinically meaningful ECG predictors.

result Improved AUROC, AUPRC, and F1 score compared to deep-learning baselines.

heart disease electrocardiogram AI interpretability machine learning

2026-03-03abs ↗pdf ↗

LU-Net: a multi-task network to improve the robustness of segmentation of left ventriclular structures by deep learning in 2D echocardiographyeess.IV

LU-Net improves cardiac segmentation accuracy and robustness.

problem Robustness and accuracy of deep learning cardiac segmentation.

method Multi-task end-to-end network designed to improve cardiac segmentation.

result Outperforms current best deep learning solution, reducing outliers and improving clinical indices.

cardiac segmentation deep learning ejection fraction left ventricle robustness

2020-04-04abs ↗pdf ↗