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Learning-Based Regularization for Cardiac Strain Analysis via Domain Adaptation
Allen Lu
, Shawn S. Ahn
, Kevinminh Ta
, Nripesh Parajuli
, John C. Stendahl
, Zhao Liu
, Nabil E. Boutagy
,
Geng-Shi Jeng
, Lawrence H. Staib
, Matthew OrDonnell
, Albert J. Sinusas
, James S. Duncan
電機工程學系
研究成果
:
Article
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同行評審
19
引文 斯高帕斯(Scopus)
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Keyphrases
Learning-based
100%
Domain Adaptation
100%
Strain Analysis
100%
Cardiac Strain
100%
Motion Estimation
66%
Regularization Method
66%
Infarct
66%
Biomechanical Constraints
66%
Semi-supervised Learning
33%
Image Properties
33%
Low SNR
33%
Early Detection
33%
Synthetic Data
33%
Supervised Neural Network
33%
Strain Mapping
33%
In Vivo Data
33%
Motion Estimate
33%
Echocardiography
33%
Targeted Intervention
33%
Myocardial Injury
33%
Latent Representation
33%
Multilayered Perceptron Network
33%
Regularization Framework
33%
Sonomicrometer
33%
Network Regularization
33%
Regional Strain
33%
Semi-supervised Regularization
33%
Engineering
Regularization
100%
Strain Analysis
100%
Motion Estimation
50%
Good Agreement
25%
Early Detection
25%
Motion Estimate
25%
Regional Strain
25%
Regularization Method
25%
Strain Map
25%
Perceptron
25%
Computer Science
Domain Adaptation
100%
Regularization
100%
Motion Estimation
50%
Neural Network
25%
Semisupervised Learning
25%
Synthetic Data
25%
Motion Estimate
25%
Early Detection
25%
Regularization Method
25%
Neuroscience
In Vivo
100%
Neural Network
100%
Perceptron
100%
Chemical Engineering
Supervised Learning
100%
Neural Network
100%
Biochemistry, Genetics and Molecular Biology
Motion
100%
Perceptron
33%