Roger C Tam

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David K. B. LiUniversity of British ColumbiaDirección de correo verificada de ubc.ca
Shannon KolindUniversity of British ColumbiaDirección de correo verificada de ubc.ca
Irene VavasourSenior Imaging Scientist, University of British ColumbiaDirección de correo verificada de phas.ubc.ca
Cornelia LauleUniversity of British ColumbiaDirección de correo verificada de physics.ubc.ca
Lisa Y.W. TangSimon Fraser University; University of British ColumbiaDirección de correo verificada de sfu.ca
Youngjin YooSiemens HealthineersDirección de correo verificada de siemens-healthineers.com
Alex MacKayProfessor,Joint Radiology/ Physics & Astronomy, University of British ColumbiaDirección de correo verificada de physics.ubc.ca
Alexander RauscherAssociate Professor, Magnetic Resonance Imaging, University of British ColumbiaDirección de correo verificada de physics.ubc.ca
Tom BroschPhilips Research HamburgDirección de correo verificada de philips.com
Adam V. DvorakDepartment of Physics and Astronomy, University of British ColumbiaDirección de correo verificada de ubc.ca
Chun Liang HsuThe Hong Kong Polytechnic University (PolyU)Dirección de correo verificada de polyu.edu.hk
Hanwen LiuUniversity of British ColumbiaDirección de correo verificada de phas.ubc.ca
Ging-Yuek Robin HsiungAssociate Professor of Medicine, University of British ColumbiaDirección de correo verificada de mail.ubc.ca
Mark S. FreedmanUniversity of OttawaDirección de correo verificada de toh.ca
Jiwon OhJohns Hopkins University, University of TorontoDirección de correo verificada de jhsph.edu
Ghassan HamarnehComputing Science, Simon Fraser UniversityDirección de correo verificada de sfu.ca
Albert HuangSr. Manager, Research Engineering, Sony Corporation of AmericaDirección de correo verificada de ece.ubc.ca
Cindy K BarhaAssistant Professor, University of CalgaryDirección de correo verificada de ucalgary.ca
Emil LjungbergMedical Radiation Physics, Lund UniversityDirección de correo verificada de med.lu.se
Rachel A. CrockettUniversity of OxfordDirección de correo verificada de ndcn.ox.ac.uk

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Roger C Tam

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

Dirección de correo verificada de ubc.ca - Página principal

Machine Learning Medical Imaging Education in Medical Device Design


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	702	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	543	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	307	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	256	2012
Spinal cord grey matter segmentation challenge F Prados, J Ashburner, C Blaiotta, T Brosch, J Carballido-Gamio, ... Neuroimage 152, 312-329, 2017	159	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	118	2015
Shape simplification based on the medial axis transform R Tam, W Heidrich IEEE Visualization, 2003. VIS 2003., 481-488, 2003	110	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	108	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	86	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	77	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	74	2021
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
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
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	72	2019
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	72	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	70	2015
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	69	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	66	2016
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	63	2021

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