Deep learning and the electrocardiogram: review of the current state-of-the-art
In the recent decade, deep learning, a subset of artificial intelligence and machine learning,
has been used to identify patterns in big healthcare datasets for disease phenotyping, event …
has been used to identify patterns in big healthcare datasets for disease phenotyping, event …
Pgans: Personalized generative adversarial networks for ecg synthesis to improve patient-specific deep ecg classification
T Golany, K Radinsky - Proceedings of the AAAI Conference on Artificial …, 2019 - aaai.org
The Electrocardiogram (ECG) is performed routinely by medical personnel to identify
structural, functional and electrical cardiac events. Many attempts were made to automate …
structural, functional and electrical cardiac events. Many attempts were made to automate …
Designing lightweight deep learning models for echocardiography view classification
Transthoracic echocardiography (echo) is the most common imaging modality for diagnosis
of cardiac conditions. Echo is acquired from a multitude of views, each of which distinctly …
of cardiac conditions. Echo is acquired from a multitude of views, each of which distinctly …
Melo: Low-rank adaptation is better than fine-tuning for medical image diagnosis
The common practice in developing computer-aided diagnosis (CAD) models based on
transformer architectures usually involves fine-tuning from ImageNet pre-trained weights …
transformer architectures usually involves fine-tuning from ImageNet pre-trained weights …
Deep learning for cardiologist-level myocardial infarction detection in electrocardiograms
Myocardial infarction is the leading cause of death worldwide. In this paper, we design
domain-inspired neural network models to detect myocardial infarction. First, we study the …
domain-inspired neural network models to detect myocardial infarction. First, we study the …
Fast and accurate view classification of echocardiograms using deep learning
Echocardiography is essential to cardiology. However, the need for human interpretation
has limited echocardiography's full potential for precision medicine. Deep learning is an …
has limited echocardiography's full potential for precision medicine. Deep learning is an …
Automatic classification of healthy and disease conditions from images or digital standard 12-lead electrocardiograms
Abstract Standard 12-lead electrocardiography (ECG) is used as the primary clinical tool to
diagnose changes in heart function. The value of automated 12-lead ECG diagnostic …
diagnose changes in heart function. The value of automated 12-lead ECG diagnostic …
scl-st: Supervised contrastive learning with semantic transformations for multiple lead ecg arrhythmia classification
The automatic classification of electrocardiogram (ECG) signals has played an important
role in cardiovascular diseases diagnosis and prediction. With recent advancements in deep …
role in cardiovascular diseases diagnosis and prediction. With recent advancements in deep …
[HTML][HTML] Real-time echocardiography image analysis and quantification of cardiac indices
Deep learning has a huge potential to transform echocardiography in clinical practice and
point of care ultrasound testing by providing real-time analysis of cardiac structure and …
point of care ultrasound testing by providing real-time analysis of cardiac structure and …
An arrhythmia classification model based on vision transformer with deformable attention
Y Dong, M Zhang, L Qiu, L Wang, Y Yu - Micromachines, 2023 - mdpi.com
The electrocardiogram (ECG) is a highly effective non-invasive tool for monitoring heart
activity and diagnosing cardiovascular diseases (CVDs). Automatic detection of arrhythmia …
activity and diagnosing cardiovascular diseases (CVDs). Automatic detection of arrhythmia …