Cargando…
A foundational vision transformer improves diagnostic performance for electrocardiograms
The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning approaches for biomedical problems may result in suboptimal performance when pre-training is done on natural images. We...
Autores principales: | Vaid, Akhil, Jiang, Joy, Sawant, Ashwin, Lerakis, Stamatios, Argulian, Edgar, Ahuja, Yuri, Lampert, Joshua, Charney, Alexander, Greenspan, Hayit, Narula, Jagat, Glicksberg, Benjamin, Nadkarni, Girish N |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10242218/ https://www.ncbi.nlm.nih.gov/pubmed/37280346 http://dx.doi.org/10.1038/s41746-023-00840-9 |
Ejemplares similares
-
Multi-center retrospective cohort study applying deep learning to electrocardiograms to identify left heart valvular dysfunction
por: Vaid, Akhil, et al.
Publicado: (2023) -
Quantitative prediction of right ventricular and size and function from the electrocardiogram
por: Duong, Son Q., et al.
Publicado: (2023) -
Deep learning and the electrocardiogram: review of the current state-of-the-art
por: Somani, Sulaiman, et al.
Publicado: (2021) -
Impact of COVID-19 Pandemic on the Role of Cardiac Sonographers
por: Garg, Vaani P., et al.
Publicado: (2021) -
Deep Learning for Echocardiography: Introduction for Clinicians and Future Vision: State-of-the-Art Review
por: Krittanawong, Chayakrit, et al.
Publicado: (2023)