| 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 | 705 | 2016 |
| Manifold learning of brain MRIs by deep learning T Brosch, R Tam, Alzheimer’s Disease Neuroimaging Initiative International conference on medical image computing and computer-assisted …, 2013 | 306 | 2013 |
| Spinal cord grey matter segmentation challenge F Prados, J Ashburner, C Blaiotta, T Brosch, J Carballido-Gamio, ... Neuroimage 152, 312-329, 2017 | 170 | 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 | 112 | 2018 |
| Runtime packers: The hidden problem T Brosch, M Morgenstern Black Hat USA, 2006 | 86 | 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 | 78 | 2014 |
| 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 | 74 | 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 |
| 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 | 60 | 2016 |
| 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 | 54 | 2020 |
| Foveal fully convolutional nets for multi-organ segmentation T Brosch, A Saalbach Medical imaging 2018: Image processing 10574, 198-206, 2018 | 48 | 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 | 47 | 2018 |
| 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 | 35 | 2018 |
| 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 | 26 | 2021 |
| Medical Image Computing and Computer-Assisted Intervention (MICCAI 2013) T Brosch, Y Yoo, DKB Li, A Traboulsee, R Tam, P Golland, N Hata, ... Springer, 2014 | 22 | 2014 |
| 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 26th Annual Meeting of ISMRM, Paris, France Abstract 1175, 2018 | 21 | 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 | 18 | 2017 |
| 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 | 15 | 2018 |
| FLAIR2 improves LesionTOADS automatic segmentation of multiple sclerosis lesions in non-homogenized, multi-center, 2D clinical magnetic resonance images M Le, LYW Tang, E Hernández-Torres, M Jarrett, T Brosch, L Metz, DKB Li, ... NeuroImage: Clinical 23, 101918, 2019 | 13 | 2019 |
| Organ-at-risk segmentation in brain MRI using model-based segmentation: benefits of deep learning-based boundary detectors E Orasanu, T Brosch, C Glide-Hurst, S Renisch International Workshop on Shape in Medical Imaging, 291-299, 2018 | 12 | 2018 |
Roger C TamAssociate Professor, School of Biomedical Engineering, University of British ColumbiaEmail được xác minh tại ubc.ca