Tom Brosch
Zitiert von
Zitiert von
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
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
Spinal cord grey matter segmentation challenge
F Prados, J Ashburner, C Blaiotta, T Brosch, J Carballido-Gamio, ...
Neuroimage 152, 312-329, 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
Runtime packers: The hidden problem
T Brosch, M Morgenstern
Black Hat USA, 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
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
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
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
Foveal fully convolutional nets for multi-organ segmentation
T Brosch, A Saalbach
Medical imaging 2018: Image processing 10574, 198-206, 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
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
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
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
Artificial intelligence-enabled localization of anatomical landmarks
F Wenzel, T Brosch
US Patent 11,475,559, 2022
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
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
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
Initiative for the Alzheimers Disease Neuroimaging
T Brosch, R Tam
Manifold learning of brain MRIs by deep learning. Med Image Comput Comput …, 2013
Runtime packers: The hidden problem
M Morgenstern
Blackhat USA 2006, 2006
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–20