| 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 | 11779 | 2016 |
| Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields PF Christ, MEA Elshaer, F Ettlinger, S Tatavarty, M Bickel, P Bilic, ... International Conference on Medical Image Computing and Computer-Assisted …, 2016 | 855 | 2016 |
| 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 | 477 | 2017 |
| Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks PF Christ, F Ettlinger, F Grün, MEA Elshaera, J Lipkova, S Schlecht, ... arXiv preprint arXiv:1702.05970, 2017 | 453 | 2017 |
| Statistical modeling and recognition of surgical workflow N Padoy, T Blum, SA Ahmadi, H Feussner, MO Berger, N Navab Medical image analysis 16 (3), 632-641, 2012 | 326 | 2012 |
| DeepVOG: Open-source pupil segmentation and gaze estimation in neuroscience using deep learning YH Yiu, M Aboulatta, T Raiser, L Ophey, VL Flanagin, P Zu Eulenburg, ... Journal of neuroscience methods 324, 108307, 2019 | 184 | 2019 |
| InceptionGCN: receptive field aware graph convolutional network for disease prediction A Kazi, S Shekarforoush, S Arvind Krishna, H Burwinkel, G Vivar, ... Information Processing in Medical Imaging: 26th International Conference …, 2019 | 181 | 2019 |
| Differentiable graph module (dgm) for graph convolutional networks A Kazi, L Cosmo, SA Ahmadi, N Navab, MM Bronstein IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (2), 1606-1617, 2022 | 176 | 2022 |
| Recovery of surgical workflow without explicit models SA Ahmadi, T Sielhorst, R Stauder, M Horn, H Feussner, N Navab Medical Image Computing and Computer-Assisted Intervention–MICCAI 2006: 9th …, 2006 | 121 | 2006 |
| 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 | 114 | 2015 |
| Latent-graph learning for disease prediction L Cosmo, A Kazi, SA Ahmadi, N Navab, M Bronstein Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 86 | 2020 |
| Connectivity patterns of pallidal DBS electrodes in focal dystonia: a diffusion tensor tractography study VE Rozanski, C Vollmar, JP Cunha, SMN Tafula, SA Ahmadi, M Patzig, ... Neuroimage 84, 435-442, 2014 | 59 | 2014 |
| MR imaging differentiation of Fe2+ and Fe3+ based on relaxation and magnetic susceptibility properties O Dietrich, J Levin, SA Ahmadi, A Plate, MF Reiser, K Bötzel, A Giese, ... Neuroradiology 59, 403-409, 2017 | 54 | 2017 |
| Graph convolution based attention model for personalized disease prediction A Kazi, S Shekarforoush, S Arvind Krishna, H Burwinkel, G Vivar, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 49 | 2019 |
| SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks PF Christ, F Ettlinger, G Kaissis, S Schlecht, F Ahmaddy, F Grün, ... 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017 …, 2017 | 48 | 2017 |
| Patient-specific 3D ultrasound simulation based on convolutional ray-tracing and appearance optimization M Salehi, SA Ahmadi, R Prevost, N Navab, W Wein Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015 …, 2015 | 42 | 2015 |
| Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders SA Ahmadi, G Vivar, N Navab, K Möhwald, A Maier, H Hadzhikolev, ... Journal of neurology 267, 143-152, 2020 | 39 | 2020 |
| A diffusion-based connectivity map of the GPi for optimised stereotactic targeting in DBS NM da Silva, SA Ahmadi, SN Tafula, JPS Cunha, K Bötzel, C Vollmar, ... Neuroimage 144, 83-91, 2017 | 39 | 2017 |
| Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer M Kawula, D Purice, M Li, G Vivar, SA Ahmadi, K Parodi, C Belka, ... Radiation Oncology 17 (1), 21, 2022 | 38 | 2022 |
| Cerebellar dizziness and vertigo: etiologies, diagnostic assessment, and treatment A Zwergal, K Feil, R Schniepp, M Strupp Seminars in Neurology 40 (01), 087-096, 2020 | 37 | 2020 |
Nassir NavabProfessor of Computer Science, Technische Universität MünchenVerified email at cs.tum.edu
