| 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-Chęnevert, 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 |
David K. B. LiUniversity of British ColumbiaVerified email at ubc.ca
