| 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 | 663 | 2016 |
| 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 | 284 | 2013 |
| Spinal cord grey matter segmentation challenge F Prados, J Ashburner, C Blaiotta, T Brosch, J Carballido-Gamio, ... Neuroimage 152, 312-329, 2017 | 145 | 2017 |
| 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 | 98 | 2018 |
| Runtime packers: The hidden problem T Brosch, M Morgenstern Black Hat USA, 2006 | 84 | 2006 |
| 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 | 73 | 2014 |
| 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 | 72 | 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 | 70 | 2014 |
| Deep learning of brain lesion patterns for predicting future disease activity in patients with early symptoms of multiple sclerosis Y Yoo, LW Tang, T Brosch, DKB Li, L Metz, A Traboulsee, R Tam Deep Learning and Data Labeling for Medical Applications: First …, 2016 | 53 | 2016 |
| Foveal fully convolutional nets for multi-organ segmentation T Brosch, A Saalbach Medical imaging 2018: Image processing 10574, 198-206, 2018 | 45 | 2018 |
| Deep learning-based boundary detection for model-based segmentation with application to MR prostate segmentation T Brosch, J Peters, A Groth, T Stehle, J Weese Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 40 | 2018 |
| Correction of motion artifacts using a multiscale fully convolutional neural network K Sommer, A Saalbach, T Brosch, C Hall, NM Cross, JB Andre American Journal of Neuroradiology 41 (3), 416-423, 2020 | 39 | 2020 |
| Iterative segmentation from limited training data: applications to congenital heart disease DF Pace, AV Dalca, T Brosch, T Geva, AJ Powell, J Weese, MH Moghari, ... Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2018 | 34 | 2018 |
| Artificial intelligence-enabled localization of anatomical landmarks F Wenzel, T Brosch US Patent 11,475,559, 2022 | 20 | 2022 |
| Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks AI Iuga, H Carolus, AJ Höink, T Brosch, T Klinder, D Maintz, T Persigehl, ... BMC Medical Imaging 21, 1-12, 2021 | 19 | 2021 |
| Correction of motion artifacts using a multi-resolution fully convolutional neural network K Sommer, T Brosch, R Wiemker, T Harder, A Saalbach, CS Hall, ... Proceedings of the ISMRM Scientific Meeting & Exhibition, Paris 1175, 2018 | 19 | 2018 |
| Deep convolutional encoder networks for multiple sclerosis lesion segmentation. Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015. MICCAI 2015 T Brosch, Y Yoo, LY Tang, DK Li, A Traboulsee, R Tam Lecture Notes in Computer Science, Springer, Cham 9351, 2015 | 15 | 2015 |
| Automated abdominal plane and circumference estimation in 3D US for fetal screening C Lorenz, T Brosch, C Ciofolo-Veit, T Klinder, T Lefevre, A Cavallaro, ... Medical Imaging 2018: Image Processing 10574, 111-119, 2018 | 13 | 2018 |
| Abdomen segmentation in 3D fetal ultrasound using CNN-powered deformable models A Schmidt-Richberg, T Brosch, N Schadewaldt, T Klinder, A Cavallaro, ... Fetal, Infant and Ophthalmic Medical Image Analysis: International Workshop …, 2017 | 12 | 2017 |
| Initiative for the Alzheimers Disease Neuroimaging T Brosch, R Tam Manifold learning of brain MRIs by deep learning. Med Image Comput Comput …, 2013 | 12 | 2013 |
Roger C TamAssociate Professor, School of Biomedical Engineering, University of British ColumbiaVerified email at ubc.ca
