| 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 | 374 | 2017 |
| Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS … D Stoyanov, Z Taylor, G Carneiro, T Syeda-Mahmood, A Martel, ... Springer, 2018 | 208 | 2018 |
| QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy AG Roy, S Conjeti, N Navab, C Wachinger, ... NeuroImage 186, 713-727, 2019 | 165* | 2019 |
| A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals RR Singh, S Conjeti, R Banerjee Biomedical Signal Processing and Control 8 (6), 740-754, 2013 | 158 | 2013 |
| Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open … PCCD Community The Lancet Oncology, 2016 | 127* | 2016 |
| Fastsurfer-a fast and accurate deep learning based neuroimaging pipeline L Henschel, S Conjeti, S Estrada, K Diers, B Fischl, M Reuter NeuroImage 219, 117012, 2020 | 107 | 2020 |
| Error corrective boosting for learning fully convolutional networks with limited data AG Roy, S Conjeti, D Sheet, A Katouzian, N Navab, C Wachinger International Conference on Medical Image Computing and Computer-Assisted …, 2017 | 87* | 2017 |
| Generalizability vs. Robustness: Investigating Medical Imaging Networks Using Adversarial Examples M Paschali, S Conjeti, F Navarro, N Navab International Conference on Medical Image Computing and Computer-Assisted …, 2018 | 80* | 2018 |
| Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control AG Roy, S Conjeti, N Navab, C Wachinger, ... NeuroImage 195, 11-22, 2019 | 64 | 2019 |
| Inherent brain segmentation quality control from fully convnet monte carlo sampling AG Roy, S Conjeti, N Navab, C Wachinger International Conference on Medical Image Computing and Computer-Assisted …, 2018 | 64 | 2018 |
| CATARACTS: Challenge on automatic tool annotation for cataRACT surgery H Al Hajj, M Lamard, PH Conze, S Roychowdhury, X Hu, G Maršalkaitė, ... Medical image analysis 52, 24-41, 2019 | 60 | 2019 |
| Multiple instance learning of deep convolutional neural networks for breast histopathology whole slide classification K Das, S Conjeti, AG Roy, J Chatterjee, D Sheet 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018 …, 2018 | 48 | 2018 |
| Survival analysis for high-dimensional, heterogeneous medical data: Exploring feature extraction as an alternative to feature selection S Pölsterl, S Conjeti, N Navab, A Katouzian Artificial intelligence in medicine 72, 1-11, 2016 | 43 | 2016 |
| An approach for real-time stress-trend detection using physiological signals in wearable computing systems for automotive drivers RR Singh, S Conjeti, R Banerjee 2011 14th International IEEE Conference on Intelligent Transportation …, 2011 | 43 | 2011 |
| Human motion analysis with deep metric learning H Coskun, DJ Tan, S Conjeti, N Navab, F Tombari Proceedings of the European Conference on Computer Vision (ECCV), 667-683, 2018 | 38 | 2018 |
| FatSegNet: A fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI S Estrada, R Lu, S Conjeti, X Orozco‐Ruiz, J Panos‐Willuhn, ... Magnetic resonance in medicine 83 (4), 1471-1483, 2020 | 37 | 2020 |
| Lumen Segmentation in Intravascular Optical Coherence Tomography using Backscattering Tracked and Initialized Random Walks A Guha Roy, S Conjeti, S Carlier, P Dutta, A Kastrati, A Laine, N Navab, ... IEEE Journal of Biomedical and Health Informatics, 2015 | 35 | 2015 |
| Heterogeneous ensembles for predicting survival of metastatic, castrate-resistant prostate cancer patients NN Sebastian Pölsterl, Pankaj Gupta, Lichao Wang, Sailesh Conjeti, Amin ... F1000Research 5 (2676), 2016 | 33* | 2016 |
| Deeply learnt hashing forests for content based image retrieval in prostate MR images A Shah, S Conjeti, N Navab, A Katouzian Medical Imaging 2016: Image Processing 9784, 978414, 2016 | 32 | 2016 |
| Assessment of driver stress from physiological signals collected under real-time semi-urban driving scenarios RR Singh, S Conjeti, R Banerjee International Journal of Computational Intelligence Systems 7 (5), 909-923, 2014 | 30 | 2014 |
Nassir NavabProfessor of Computer Science, Technische Universität MünchenVerified email at cs.tum.edu
