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Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging

Frontiers in cardiovascular medicine, 2022-11, Vol.9, p.1016032-1016032

2022

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Advancing Subclinical Hypertrophic Cardiomyopathy Diagnosis: The Role of Cardiovascular Magnetic Resonance Based Radiomics and Machine Learning

Journal of cardiovascular magnetic resonance, 2024, Vol.26, p.100726, Article 100726

2024

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Atrial fibrillation prediction by combining ECG markers and CMR radiomics

Scientific reports, 2022-11, Vol.12 (1), p.18876-18876, Article 18876

2022

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Iterated random walks with shape prior

Image and vision computing, 2016-10, Vol.54, p.12-21

2016

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Random walks with statistical shape prior for cochlea and inner ear segmentation in micro-CT images

Machine vision and applications, 2018-04, Vol.29 (3), p.405-414

2018

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Random walks with shape prior for cochlea segmentation in ex vivo μCT

International journal for computer assisted radiology and surgery, 2016-09, Vol.11 (9), p.1647-1659

2016

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Shape-Based Normalized Cuts Using Spectral Relaxation for Biomedical Segmentation

IEEE transactions on image processing, 2014-01, Vol.23 (1), p.163-170

2014

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Kiosk 6R-FA-02 - Advancing Subclinical Hypertrophic Cardiomyopathy Diagnosis: The Role of Cardiovascular Magnetic Resonance Based Radiomics and Machine Learning

Journal of cardiovascular magnetic resonance, 2024, Vol.26

2024

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Statistical Shape Model with Random Walks for Inner Ear Segmentation

Spectral and Shape Analysis in Medical Imaging, p.92-102

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Predicting left ventricular hypertrophy from the 12-lead electrocardiogram in the UK Biobank imaging study using machine learning

European heart journal. Digital health, 2023-08, Vol.4 (4), p.316-324

2023

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Random walks with shape prior for cochlea segmentation in ex vivo $$\mu \hbox {CT}$$ μ CT

International journal for computer assisted radiology and surgery, 2016-09, Vol.11 (9), p.1647-1659

2016

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Medical image segmentation with shape priors

Ruiz Pujadas, Esmeralda

2014

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Prediction of Incident Atrial Fibrillation in Population with Ischemic Heart Disease Using Machine Learning with Radiomics and ECG Markers

Medical Image Understanding and Analysis, p.441-453

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Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging

Advancing Subclinical Hypertrophic Cardiomyopathy Diagnosis: The Role of Cardiovascular Magnetic Resonance Based Radiomics and Machine Learning

Atrial fibrillation prediction by combining ECG markers and CMR radiomics

Iterated random walks with shape prior

Random walks with statistical shape prior for cochlea and inner ear segmentation in micro-CT images

Random walks with shape prior for cochlea segmentation in ex vivo μCT

Shape-Based Normalized Cuts Using Spectral Relaxation for Biomedical Segmentation

Kiosk 6R-FA-02 - Advancing Subclinical Hypertrophic Cardiomyopathy Diagnosis: The Role of Cardiovascular Magnetic Resonance Based Radiomics and Machine Learning

Statistical Shape Model with Random Walks for Inner Ear Segmentation

Predicting left ventricular hypertrophy from the 12-lead electrocardiogram in the UK Biobank imaging study using machine learning

Random walks with shape prior for cochlea segmentation in ex vivo $$\mu \hbox {CT}$$ μ CT

Medical image segmentation with shape priors

Prediction of Incident Atrial Fibrillation in Population with Ischemic Heart Disease Using Machine Learning with Radiomics and ECG Markers

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