A joint cross-dimensional contrastive learning framework for 12-lead ECGs and its heterogeneous deployment on SoC-Reference-Cited by-同舟云学术

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A joint cross-dimensional contrastive learning framework for 12-lead ECGs and its heterogeneous deployment on SoC

Published:2023-01 Issue: Volume:152 Page:106390
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Liu Wenhan,Zhang Huaicheng,Chang Sheng,Wang Hao,He Jin,Huang Qijun

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference41 articles.

1. Self-supervised visual feature learning with deep neural networks: a survey;Jing;IEEE Trans. Pattern Anal. Mach. Intell.,2021

2. A simple framework for contrastive learning of visual representations;Chen;Int. Conf. Mach. Learn.,2020

3. Representation Learning with Contrastive Predictive Coding;den Oord,2018

4. Momentum contrast for unsupervised visual representation learning;He,2020

5. Data-efficient image recognition with contrastive predictive coding;Henaff;Int. Conf. Mach. Learn.,2020

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Conv-RGNN: An efficient Convolutional Residual Graph Neural Network for ECG classification;Computer Methods and Programs in Biomedicine;2024-12

2. Learning Representations for Multilead Electrocardiograms From Morphology-Rhythm Contrast;IEEE Transactions on Instrumentation and Measurement;2024

3. Applications of Self-Supervised Learning to Biomedical Signals: A Survey;IEEE Access;2023

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