Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory

Lianrui Zuo; Blake E. Dewey; Yihao Liu; Yufan He; Scott D. Newsome; Ellen M. Mowry; Susan M. Resnick; Jerry L. Prince; Aaron Carass

doi:10.1016/j.neuroimage.2021.118569

JOURNAL ARTICLE

Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory

Lianrui Zuo Blake E. Dewey Yihao Liu Yufan He Scott D. Newsome Ellen M. Mowry Susan M. Resnick Jerry L. Prince Aaron Carass

Year: 2021 Journal: NeuroImage Vol: 243 Pages: 118569-118569 Publisher: Elsevier BV

DOI: 10.1016/j.neuroimage.2021.118569

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Keywords:

Harmonization Contrast (vision) Computer science Bottleneck Artificial intelligence Standardization Sample (material) Population Pattern recognition (psychology) Computer vision Medicine

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Advanced MRI Techniques and Applications

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Photoacoustic and Ultrasonic Imaging

Physical Sciences → Engineering → Biomedical Engineering

Sparse and Compressive Sensing Techniques

Physical Sciences → Engineering → Computational Mechanics

Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory

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