Sledovat
Nassir Navab
Nassir Navab
Professor of Computer Science, Technische Universität München
E-mailová adresa ověřena na: cs.tum.edu - Domovská stránka
Název
Citace
Citace
Rok
V-net: Fully convolutional neural networks for volumetric medical image segmentation
F Milletari, N Navab, SA Ahmadi
2016 fourth international conference on 3D vision (3DV), 565-571, 2016
119262016
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
BE Bejnordi, M Veta, PJ Van Diest, B Van Ginneken, N Karssemeijer, ...
Jama 318 (22), 2199-2210, 2017
33872017
Deeper depth prediction with fully convolutional residual networks
I Laina, C Rupprecht, V Belagiannis, F Tombari, N Navab
2016 Fourth international conference on 3D vision (3DV), 239-248, 2016
24152016
Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes
S Hinterstoisser, V Lepetit, S Ilic, S Holzer, G Bradski, K Konolige, ...
Asian conference on computer vision, 548-562, 2012
17152012
Model globally, match locally: Efficient and robust 3D object recognition
B Drost, M Ulrich, N Navab, S Ilic
2010 IEEE computer society conference on computer vision and pattern …, 2010
13112010
Ssd-6d: Making rgb-based 3d detection and 6d pose estimation great again
W Kehl, F Manhardt, F Tombari, S Ilic, N Navab
Proceedings of the IEEE international conference on computer vision, 1521-1529, 2017
12572017
Concurrent spatial and channel ‘squeeze & excitation’in fully convolutional networks
AG Roy, N Navab, C Wachinger
Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018
11762018
Cnn-slam: Real-time dense monocular slam with learned depth prediction
K Tateno, F Tombari, I Laina, N Navab
Proceedings of the IEEE conference on computer vision and pattern …, 2017
9992017
Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes
S Hinterstoisser, S Holzer, C Cagniart, S Ilic, K Konolige, N Navab, ...
2011 international conference on computer vision, 858-865, 2011
8662011
Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: evaluation with PET/CT data
A Martinez-Möller, M Souvatzoglou, G Delso, RA Bundschuh, ...
Journal of nuclear medicine 50 (4), 520-526, 2009
8342009
Structure-preserving color normalization and sparse stain separation for histological images
A Vahadane, T Peng, A Sethi, S Albarqouni, L Wang, M Baust, K Steiger, ...
IEEE transactions on medical imaging 35 (8), 1962-1971, 2016
8282016
Gradient response maps for real-time detection of textureless objects
S Hinterstoisser, C Cagniart, S Ilic, P Sturm, N Navab, P Fua, V Lepetit
IEEE transactions on pattern analysis and machine intelligence 34 (5), 876-888, 2011
8062011
Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images
S Albarqouni, C Baur, F Achilles, V Belagiannis, S Demirci, N Navab
IEEE transactions on medical imaging 35 (5), 1313-1321, 2016
7192016
ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks
AG Roy, S Conjeti, SPK Karri, D Sheet, A Katouzian, C Wachinger, ...
Biomedical optics express 8 (8), 3627-3642, 2017
6492017
Deep autoencoding models for unsupervised anomaly segmentation in brain MR images
C Baur, B Wiestler, S Albarqouni, N Navab
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2019
6332019
Dense image registration through MRFs and efficient linear programming
B Glocker, N Komodakis, G Tziritas, N Navab, N Paragios
Medical image analysis 12 (6), 731-741, 2008
5732008
GANs for medical image analysis
S Kazeminia, C Baur, A Kuijper, B Van Ginneken, N Navab, S Albarqouni, ...
Artificial intelligence in medicine 109, 101938, 2020
5682020
Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge
K Murphy, B Van Ginneken, JM Reinhardt, S Kabus, K Ding, X Deng, ...
IEEE transactions on medical imaging 30 (11), 1901-1920, 2011
5282011
Recalibrating fully convolutional networks with spatial and channel “squeeze and excitation” blocks
AG Roy, N Navab, C Wachinger
IEEE transactions on medical imaging 38 (2), 540-549, 2018
5122018
Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound
F Milletari, SA Ahmadi, C Kroll, A Plate, V Rozanski, J Maiostre, J Levin, ...
Computer Vision and Image Understanding 164, 92-102, 2017
4872017
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Články 1–20