Applying masked autoencoder-based self-supervised learning for high-capability vision transformers of electrocardiographies.

Applying masked autoencoder-based self-supervised learning for high-capability vision transformers of electrocardiographies.

The generalization of deep neural network algorithms to a broader population is an important challenge in the medical field. We aimed to apply self-supervised learning using masked autoencoders (MAEs) to improve the performance of the 12-lead electrocardiography (ECG) analysis model using limited EC...

Full description

Saved in:
Bibliographic Details
Main Authors: Shinnosuke Sawano, Satoshi Kodera, Naoto Setoguchi, Kengo Tanabe, Shunichi Kushida, Junji Kanda, Mike Saji, Mamoru Nanasato, Hisataka Maki, Hideo Fujita, Nahoko Kato, Hiroyuki Watanabe, Minami Suzuki, Masao Takahashi, Naoko Sawada, Masao Yamasaki, Masataka Sato, Susumu Katsushika, Hiroki Shinohara, Norifumi Takeda, Katsuhito Fujiu, Masao Daimon, Hiroshi Akazawa, Hiroyuki Morita, Issei Komuro
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2024-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0307978
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1371/journal.pone.0307978

Similar Items