| 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 | 12028 | 2016 |
| 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 | 3407 | 2017 |
| 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 | 2424 | 2016 |
| 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 | 1730 | 2012 |
| 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 | 1321 | 2010 |
| 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 | 1266 | 2017 |
| 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 | 1187 | 2018 |
| 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 | 1000 | 2017 |
| 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 | 871 | 2011 |
| 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 | 837 | 2009 |
| 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 | 835 | 2016 |
| 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 | 806 | 2011 |
| 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 | 719 | 2016 |
| 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 | 651 | 2017 |
| 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 | 637 | 2019 |
| 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 | 573 | 2020 |
| 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 | 573 | 2008 |
| 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 | 528 | 2011 |
| 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 | 515 | 2018 |
| 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 | 488 | 2017 |
Federico TombariGoogle, TU MunichEmail confirmado em in.tum.de
