Roger C Tam

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David K. B. LiUniversity of British ColumbiaAdresse e-mail validée de ubc.ca
Shannon KolindUniversity of British ColumbiaAdresse e-mail validée de ubc.ca
Irene VavasourSenior Imaging Scientist, University of British ColumbiaAdresse e-mail validée de phas.ubc.ca
Cornelia LauleUniversity of British ColumbiaAdresse e-mail validée de physics.ubc.ca
Lisa Y.W. TangSimon Fraser University; University of British ColumbiaAdresse e-mail validée de sfu.ca
Youngjin YooSiemens HealthineersAdresse e-mail validée de siemens-healthineers.com
Alex MacKayProfessor,Joint Radiology/ Physics & Astronomy, University of British ColumbiaAdresse e-mail validée de physics.ubc.ca
Alexander RauscherAssociate Professor, Magnetic Resonance Imaging, University of British ColumbiaAdresse e-mail validée de physics.ubc.ca
Tom BroschPhilips Research HamburgAdresse e-mail validée de philips.com
Adam V. DvorakDepartment of Physics and Astronomy, University of British ColumbiaAdresse e-mail validée de ubc.ca
Chun Liang HsuThe Hong Kong Polytechnic University (PolyU)Adresse e-mail validée de polyu.edu.hk
Hanwen LiuUniversity of British ColumbiaAdresse e-mail validée de phas.ubc.ca
Ging-Yuek Robin HsiungAssociate Professor of Medicine, University of British ColumbiaAdresse e-mail validée de mail.ubc.ca
Mark S. FreedmanUniversity of OttawaAdresse e-mail validée de toh.ca
Jiwon OhJohns Hopkins University, University of TorontoAdresse e-mail validée de jhsph.edu
Ghassan HamarnehComputing Science, Simon Fraser UniversityAdresse e-mail validée de sfu.ca
Albert HuangSr. Manager, Research Engineering, Sony Corporation of AmericaAdresse e-mail validée de ece.ubc.ca
Cindy K BarhaAssistant Professor, University of CalgaryAdresse e-mail validée de ucalgary.ca
Erin Leigh MacMillanMRI Clinical Scientist, The University of British ColumbiaAdresse e-mail validée de ubc.ca
Rachel A. CrockettUniversity of OxfordAdresse e-mail validée de ndcn.ox.ac.uk

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Roger C Tam

Associate Professor, School of Biomedical Engineering, University of British Columbia

Adresse e-mail validée de ubc.ca - Page d'accueil

Machine Learning Medical Imaging Education in Medical Device Design


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	712	2016
Canadian Association of Radiologists white paper on artificial intelligence in radiology A Tang, R Tam, A Cadrin-Chênevert, W Guest, J Chong, J Barfett, ... Canadian Association of Radiologists Journal 69 (2), 120-135, 2018	556	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	311	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	258	2012
Spinal cord grey matter segmentation challenge F Prados, J Ashburner, C Blaiotta, T Brosch, J Carballido-Gamio, ... Neuroimage 152, 312-329, 2017	166	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	123	2015
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	113	2018
Shape simplification based on the medial axis transform R Tam, W Heidrich IEEE Visualization, 2003. VIS 2003., 481-488, 2003	111	2003
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	91	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	84	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	78	2014
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	75	2021
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	74	2019
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	74	2019
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	74	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	73	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	73	2009
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	70	2015
Deep grey matter injury in multiple sclerosis: a NAIMS consensus statement D Ontaneda, PC Raza, KR Mahajan, DL Arnold, MG Dwyer, SA Gauthier, ... Brain 144 (7), 1974-1984, 2021	67	2021
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	67	2016

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