Cargando…
Biometric Contrastive Learning for Data-Efficient Deep Learning from Electrocardiographic Images
OBJECTIVE: Artificial intelligence (AI) detects heart disease from images of electrocardiograms (ECGs), however traditional supervised learning is limited by the need for large amounts of labeled data. We report the development of Biometric Contrastive Learning (BCL), a self-supervised pretraining a...
Autores principales: | Sangha, Veer, Khunte, Akshay, Holste, Gregory, Mortazavi, Bobak J, Wang, Zhangyang, Oikonomou, Evangelos K, Khera, Rohan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516080/ https://www.ncbi.nlm.nih.gov/pubmed/37745527 http://dx.doi.org/10.1101/2023.09.13.23295494 |
Ejemplares similares
-
Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices
por: Khunte, Akshay, et al.
Publicado: (2023) -
Automated multilabel diagnosis on electrocardiographic images and signals
por: Sangha, Veer, et al.
Publicado: (2022) -
A digital biomarker for aortic stenosis development and progression using deep learning for two-dimensional echocardiography
por: Oikonomou, Evangelos K., et al.
Publicado: (2023) -
Deep learning for biometrics
por: Bhanu, Bir, et al.
Publicado: (2017) -
Machine learning in precision diabetes care and cardiovascular risk prediction
por: Oikonomou, Evangelos K., et al.
Publicado: (2023)