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Tom Brosch

Showing results (1-10 of 11) with videos related to

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Neural Computation|November 8, 2014
Efficient training of convolutional deep belief networks in the frequency domain for application to high-resolution 2D and 3D imagesTom Brosch, Roger Tam
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention|March 1, 2014
Manifold learning of brain MRIs by deep learningTom Brosch, Roger Tam,
Shape in Medical Imaging : International Workshop, Shapemi 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018 : Proceedings. Shapemi (Workshop) (2018 : Granada, Spain)|May 17, 2019
Organ-At-Risk Segmentation in Brain MRI using Model-Based Segmentation: Benefits of Deep Learning-Based Boundary DetectorsEliza Orasanu, Tom Brosch, Carri Glide-Hurst, et al.
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention|December 9, 2014
Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learningTom Brosch, Youngjin Yoo, David K B Li, et al.
IEEE Transactions on Medical Imaging|February 18, 2016
Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion SegmentationTom Brosch, Lisa Y W Tang, Youngjin Yoo, et al.
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4Th International Workshop, DLMIA 2018, and 8Th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, S|June 8, 2019
Iterative Segmentation from Limited Training Data: Applications to Congenital Heart DiseaseDanielle F Pace, Adrian V Dalca, Tom Brosch, et al.
BMC Medical Imaging|April 14, 2021
Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networksAndra-Iza Iuga, Heike Carolus, Anna J Höink, et al.
Medical Image Analysis|May 31, 2022
Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart diseaseDanielle F Pace, Adrian V Dalca, Tom Brosch, et al.
Neuroimage. Clinical|October 27, 2017
Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controlsYoungjin Yoo, Lisa Y W Tang, Tom Brosch, et al.
Frontiers in Cardiovascular Medicine|October 31, 2023
Automated segmentation of 3D cine cardiovascular magnetic resonance imagingSoroosh Tayebi Arasteh, Jennifer Romanowicz, Danielle F Pace, et al.
Pageof 2

Showing results (1-10 of 11) with videos related to

Sort By:
Pageof 2
Neural Computation|November 8, 2014
Efficient training of convolutional deep belief networks in the frequency domain for application to high-resolution 2D and 3D imagesTom Brosch, Roger Tam
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention|March 1, 2014
Manifold learning of brain MRIs by deep learningTom Brosch, Roger Tam,
Shape in Medical Imaging : International Workshop, Shapemi 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018 : Proceedings. Shapemi (Workshop) (2018 : Granada, Spain)|May 17, 2019
Organ-At-Risk Segmentation in Brain MRI using Model-Based Segmentation: Benefits of Deep Learning-Based Boundary DetectorsEliza Orasanu, Tom Brosch, Carri Glide-Hurst, et al.
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention|December 9, 2014
Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learningTom Brosch, Youngjin Yoo, David K B Li, et al.
IEEE Transactions on Medical Imaging|February 18, 2016
Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion SegmentationTom Brosch, Lisa Y W Tang, Youngjin Yoo, et al.
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4Th International Workshop, DLMIA 2018, and 8Th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, S|June 8, 2019
Iterative Segmentation from Limited Training Data: Applications to Congenital Heart DiseaseDanielle F Pace, Adrian V Dalca, Tom Brosch, et al.
BMC Medical Imaging|April 14, 2021
Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networksAndra-Iza Iuga, Heike Carolus, Anna J Höink, et al.
Medical Image Analysis|May 31, 2022
Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart diseaseDanielle F Pace, Adrian V Dalca, Tom Brosch, et al.
Neuroimage. Clinical|October 27, 2017
Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controlsYoungjin Yoo, Lisa Y W Tang, Tom Brosch, et al.
Frontiers in Cardiovascular Medicine|October 31, 2023
Automated segmentation of 3D cine cardiovascular magnetic resonance imagingSoroosh Tayebi Arasteh, Jennifer Romanowicz, Danielle F Pace, et al.
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