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Tom Brosch
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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
5662016
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
2252013
Spinal cord grey matter segmentation challenge
F Prados, J Ashburner, C Blaiotta, T Brosch, J Carballido-Gamio, ...
Neuroimage 152, 312-329, 2017
1112017
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
812018
Runtime packers: The hidden problem
T Brosch, M Morgenstern
Black Hat USA, 2006
802006
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
662015
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
International Conference on Medical Image Computing and Computer-Assisted …, 2014
632014
Deep learning of image features from unlabeled data for multiple sclerosis lesion segmentation
Y Yoo, T Brosch, A Traboulsee, DKB Li, R Tam
International workshop on machine learning in medical imaging, 117-124, 2014
562014
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, 86-94, 2016
432016
Foveal fully convolutional nets for multi-organ segmentation
T Brosch, A Saalbach
Medical imaging 2018: Image processing 10574, 198-206, 2018
372018
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
International Conference on Medical Image Computing and Computer-Assisted …, 2018
332018
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
232018
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
222020
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
152018
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
152015
Initiative for the Alzheimers Disease Neuroimaging
T Brosch, R Tam
Manifold learning of brain MRIs by deep learning. Med Image Comput Comput …, 2013
122013
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
112018
Runtime packers: The hidden problem
M Morgenstern
Blackhat USA 2006, 2006
112006
Grey matter segmentation in spinal cord MRIs via 3D convolutional encoder networks with shortcut connections
A Porisky, T Brosch, E Ljungberg, LYW Tang, Y Yoo, BD Leener, ...
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2017
102017
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, 52-61, 2017
102017
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