Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encoders
Abstract Aims Deep neural networks (DNNs) perform excellently in interpreting
electrocardiograms (ECGs), both for conventional ECG interpretation and for novel …
electrocardiograms (ECGs), both for conventional ECG interpretation and for novel …
Cardiac arrhythmia recognition using transfer learning with a pre-trained DenseNet
Recent findings demonstrated that deep neural networks carry out features extraction itself
to identify the electrocardiography (ECG) pattern or cardiac arrhythmias from the ECG …
to identify the electrocardiography (ECG) pattern or cardiac arrhythmias from the ECG …
Simultaneous right ventricle end-diastolic and end-systolic frame identification and landmark detection on echocardiography
Z Wang, J Shi, X Hao, K Wen, X Jin… - 2021 43rd Annual …, 2021 - ieeexplore.ieee.org
End-diastolic (ED) and end-systolic (ES) frame identification and landmark detection are
crucial steps of estimating right ventricle function in clinic practice. However, the complex …
crucial steps of estimating right ventricle function in clinic practice. However, the complex …
Echoquan-net: direct quantification of echo sequence for left ventricle multidimensional indices via global-local learning, geometric adjustment and multi-target …
Accurately quantifying multidimensional indices of the left ventricle (LV) in 2D
echocardiography (echo) is clinically significant for cardiac disease diagnosis. However, the …
echocardiography (echo) is clinically significant for cardiac disease diagnosis. However, the …
Deep learning models for calculation of cardiothoracic ratio from chest radiographs for assisted diagnosis of cardiomegaly
We propose an automated method based on deep learning to compute the cardiothoracic
ratio and detect the presence of cardiomegaly from chest radiographs. We develop two …
ratio and detect the presence of cardiomegaly from chest radiographs. We develop two …
Real-time automatic ejection fraction and foreshortening detection using deep learning
Volume and ejection fraction (EF) measurements of the left ventricle (LV) in 2-D
echocardiography are associated with a high uncertainty not only due to interobserver …
echocardiography are associated with a high uncertainty not only due to interobserver …
[HTML][HTML] Validation of machine learning models for estimation of left ventricular ejection fraction on point-of-care ultrasound: insights on features that impact …
Background Machine learning (ML) algorithms can accurately estimate left ventricular
ejection fraction (LVEF) from echocardiography, but their performance on cardiac point-of …
ejection fraction (LVEF) from echocardiography, but their performance on cardiac point-of …
Inherently explainable deep neural network-based interpretation of electrocardiograms using variational auto-encoders
Abstract Background Deep neural networks (DNNs) show excellent performance in
interpreting electrocardiograms (ECGs), both for conventional ECG interpretation and for …
interpreting electrocardiograms (ECGs), both for conventional ECG interpretation and for …
[HTML][HTML] Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease
A Madani, JR Ong, A Tibrewal, MRK Mofrad - NPJ digital medicine, 2018 - nature.com
Deep learning and computer vision algorithms can deliver highly accurate and automated
interpretation of medical imaging to augment and assist clinicians. However, medical …
interpretation of medical imaging to augment and assist clinicians. However, medical …
Multislice left ventricular ejection fraction prediction from cardiac MRIs without segmentation using shared SptDenNet
We propose a spatiotemporal model for cardiac magnetic resonance images (MRI) named
SptDenNet. The proposed model is based on DenseNet and extracts spatial and temporal …
SptDenNet. The proposed model is based on DenseNet and extracts spatial and temporal …