| 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 | 8689 | 2016 |
| The future of digital health with federated learning N Rieke, J Hancox, W Li, F Milletari, HR Roth, S Albarqouni, S Bakas, ... NPJ digital medicine 3 (1), 119, 2020 | 1157 | 2020 |
| Privacy-preserving federated brain tumour segmentation W Li, F Milletarì, D Xu, N Rieke, J Hancox, W Zhu, M Baust, Y Cheng, ... Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019 …, 2019 | 435 | 2019 |
| 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 | 427 | 2017 |
| Deep learning of local rgb-d patches for 3d object detection and 6d pose estimation W Kehl, F Milletari, F Tombari, S Ilic, N Navab Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 320 | 2016 |
| 2016 fourth international conference on 3D vision (3DV) F Milletari, N Navab, SA Ahmadi IEEE, 2016 | 182 | 2016 |
| Standardized evaluation system for left ventricular segmentation algorithms in 3D echocardiography O Bernard, JG Bosch, B Heyde, M Alessandrini, D Barbosa, ... IEEE transactions on medical imaging 35 (4), 967-977, 2015 | 103 | 2015 |
| Machine learning in medical imaging W Li, F Milletarì, D Xu, N Rieke, J Hancox, W Zhu, M Baust, Y Cheng, ... 10th international workshop, MLMI, 133-141, 2019 | 93 | 2019 |
| Multimodal image-guided prostate fusion biopsy based on automatic deformable registration O Zettinig, A Shah, C Hennersperger, M Eiber, C Kroll, H Kübler, T Maurer, ... International journal of computer assisted radiology and surgery 10, 1997-2007, 2015 | 83 | 2015 |
| Interactive segmentation of medical images through fully convolutional neural networks T Sakinis, F Milletari, H Roth, P Korfiatis, P Kostandy, K Philbrick, Z Akkus, ... arXiv preprint arXiv:1903.08205, 2019 | 81 | 2019 |
| Integrating statistical prior knowledge into convolutional neural networks F Milletari, A Rothberg, J Jia, M Sofka Medical Image Computing and Computer Assisted Intervention− MICCAI 2017 …, 2017 | 51 | 2017 |
| CFCM: segmentation via coarse to fine context memory F Milletari, N Rieke, M Baust, M Esposito, N Navab Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 49 | 2018 |
| Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics M Wenzel, F Milletari, J Krüger, C Lange, M Schenk, I Apostolova, ... European journal of nuclear medicine and molecular imaging 46, 2800-2811, 2019 | 46 | 2019 |
| Searching learning strategy with reinforcement learning for 3D medical image segmentation D Yang, H Roth, Z Xu, F Milletari, L Zhang, D Xu Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 45 | 2019 |
| 3D intra-operative ultrasound and MR image guidance: pursuing an ultrasound-based management of brainshift to enhance neuronavigation M Riva, C Hennersperger, F Milletari, A Katouzian, F Pessina, ... International journal of computer assisted radiology and surgery 12, 1711-1725, 2017 | 45 | 2017 |
| Fully convolutional regression network for accurate detection of measurement points M Sofka, F Milletari, J Jia, A Rothberg Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2017 | 34 | 2017 |
| Neurreg: Neural registration and its application to image segmentation W Zhu, A Myronenko, Z Xu, W Li, H Roth, Y Huang, F Milletari, D Xu Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020 | 30 | 2020 |
| Straight to the point: Reinforcement learning for user guidance in ultrasound F Milletari, V Birodkar, M Sofka Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image …, 2019 | 24 | 2019 |
| V-Net: Fully convolutional neural networks for volumetric medical image segmentation. arXiv 2016 F Milletari, N Navab, SA Ahmadi arXiv preprint arXiv:1606.04797, 0 | 24 | |
| Weakly supervised segmentation from extreme points H Roth, L Zhang, D Yang, F Milletari, Z Xu, X Wang, D Xu Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and …, 2019 | 23 | 2019 |
Nassir NavabProfessor of Computer Science, Technische Universität MünchenПотвърден имейл адрес: cs.tum.edu
