Roger C Tam
Roger C Tam
Associate Professor, School of Biomedical Engineering, University of British Columbia
Verified email at - Homepage
Cited by
Cited by
Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation
T Brosch, LYW Tang, Y Yoo, DKB Li, A Traboulsee, R Tam
IEEE transactions on medical imaging 35 (5), 1229-1239, 2016
Canadian Association of Radiologists white paper on artificial intelligence in radiology
A Tang, R Tam, A Cadrin-Chęnevert, W Guest, J Chong, J Barfett, ...
Canadian Association of Radiologists Journal 69 (2), 120-135, 2018
Manifold learning of brain MRIs by deep learning
T Brosch, R Tam, Alzheimer’s Disease Neuroimaging Initiative
Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th …, 2013
The association between cognitive function and white matter lesion location in older adults: a systematic review
N Bolandzadeh, JC Davis, R Tam, TC Handy, T Liu-Ambrose
BMC neurology 12 (1), 126, 2012
Spinal cord grey matter segmentation challenge
F Prados, J Ashburner, C Blaiotta, T Brosch, J Carballido-Gamio, ...
Neuroimage 152, 312-329, 2017
Resistance training and white matter lesion progression in older women: exploratory analysis of a 12‐month randomized controlled trial
N Bolandzadeh, R Tam, TC Handy, LS Nagamatsu, CL Hsu, JC Davis, ...
Journal of the American Geriatrics Society 63 (10), 2052-2060, 2015
Shape simplification based on the medial axis transform
R Tam, W Heidrich
IEEE Visualization, 2003. VIS 2003., 481-488, 2003
Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls
Y Yoo, LYW Tang, T Brosch, DKB Li, S Kolind, I Vavasour, A Rauscher, ...
NeuroImage: Clinical 17, 169-178, 2018
Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT
LYW Tang, HO Coxson, S Lam, J Leipsic, RC Tam, DD Sin
The Lancet Digital Health 2 (5), e259-e267, 2020
Reproducibility of myelin water fraction analysis: a comparison of region of interest and voxel-based analysis methods
SM Meyers, C Laule, IM Vavasour, SH Kolind, B Mädler, R Tam, ...
Magnetic resonance imaging 27 (8), 1096-1103, 2009
Deep learning of image features from unlabeled data for multiple sclerosis lesion segmentation
Y Yoo, T Brosch, A Traboulsee, DKB Li, R Tam
Machine Learning in Medical Imaging: 5th International Workshop, MLMI 2014 …, 2014
A hybrid geometric–statistical deformable model for automated 3-D segmentation in brain MRI
A Huang, R Abugharbieh, R Tam
IEEE Transactions on Biomedical Engineering 56 (7), 1838-1848, 2009
Efficient training of convolutional deep belief networks in the frequency domain for application to high-resolution 2D and 3D images
T Brosch, R Tam
Neural computation 27 (1), 211-227, 2015
Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learning
T Brosch, Y Yoo, DKB Li, A Traboulsee, R Tam
Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th …, 2014
Brain and cord myelin water imaging: a progressive multiple sclerosis biomarker
S Kolind, A Seddigh, A Combes, B Russell-Schulz, R Tam, ...
NeuroImage: Clinical 9, 574-580, 2015
Machine learning in secondary progressive multiple sclerosis: an improved predictive model for short-term disability progression
MTK Law, AL Traboulsee, DKB Li, RL Carruthers, MS Freedman, ...
Multiple Sclerosis Journal–Experimental, Translational and Clinical 5 (4 …, 2019
A prospective pilot investigation of brain volume, white matter hyperintensities, and hemorrhagic lesions after mild traumatic brain injury
M Jarrett, R Tam, E Hernández-Torres, N Martin, W Perera, Y Zhao, ...
Frontiers in neurology 7, 11, 2016
An atlas for human brain myelin content throughout the adult life span
AV Dvorak, T Swift-LaPointe, IM Vavasour, LE Lee, S Abel, ...
Scientific reports 11 (1), 269, 2021
Deep learning of brain lesion patterns and user-defined clinical and MRI features for predicting conversion to multiple sclerosis from clinically isolated syndrome
Y Yoo, LYW Tang, DKB Li, L Metz, S Kolind, AL Traboulsee, RC Tam
Computer Methods in Biomechanics and Biomedical Engineering: Imaging …, 2019
Quantitative neuroimaging measures of myelin in the healthy brain and in multiple sclerosis
J O'Muircheartaigh, I Vavasour, E Ljungberg, DKB Li, A Rauscher, ...
Human Brain Mapping 40 (7), 2104-2116, 2019
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