Classifying MRI motion severity using a stacked ensemble approach-Reference-Cited by-同舟云学术

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Classifying MRI motion severity using a stacked ensemble approach

Published:2021-01 Issue: Volume:75 Page:107-115
ISSN:0730-725X
Container-title:Magnetic Resonance Imaging
language:en
Short-container-title:Magnetic Resonance Imaging

Author:

Mohebbian MohammadReza,Walia Ekta,Habibullah Mohammad,Stapleton Shawn,Wahid Khan A.

Funder

National Sciences and Engineering Research Council of Canada

Publisher

Elsevier BV

Subject

Radiology, Nuclear Medicine and imaging,Biomedical Engineering,Biophysics

Reference31 articles.

1. A survey of MRI-based medical image analysis for brain tumor studies;Bauer;Physics in Medicine & Biology,2013

2. 3D printed renal cancer models derived from MRI data: application in pre-surgical planning;Wake;Abdominal Radiology,2017

3. Automated image quality evaluation of T2-weighted liver MRI utilizing deep learning architecture;Esses;J Magn Reson Imaging,2018

4. MRIQC: advancing the automatic prediction of image quality in MRI from unseen sites;Esteban;PloS One,2017

5. MRI noise estimation and denoising using non-local PCA;Manjón;Med Image Anal,2015

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