| 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 | 625 | 2017 |
| 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 | 433 | 2020 |
| 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 | 291* | 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 | 227 | 2013 |
| Generalizability vs. Robustness: Investigating Medical Imaging Networks Using Adversarial Examples M Paschali, S Conjeti, F Navarro, N Navab Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 207* | 2018 |
| 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 | 170* | 2016 |
| 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 | 152 | 2019 |
| 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 | 125 | 2019 |
| Error corrective boosting for learning fully convolutional networks with limited data AG Roy, S Conjeti, D Sheet, A Katouzian, N Navab, C Wachinger Medical Image Computing and Computer Assisted Intervention− MICCAI 2017 …, 2017 | 110* | 2017 |
| 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 | 105 | 2018 |
| Inherent brain segmentation quality control from fully convnet monte carlo sampling AG Roy, S Conjeti, N Navab, C Wachinger Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 102 | 2018 |
| FatSegNet: A fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI S Estrada, R Lu, S Conjeti, X OrozcoRuiz, J PanosWilluhn, ... Magnetic resonance in medicine 83 (4), 1471-1483, 2020 | 86 | 2020 |
| Medical Image Computing and Computer Assisted Intervention− MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III M Descoteaux, L Maier-Hein, A Franz, P Jannin, DL Collins, S Duchesne Springer, 2017 | 71 | 2017 |
| An artificial intelligence–based chest X-ray model on human nodule detection accuracy from a multicenter study F Homayounieh, S Digumarthy, S Ebrahimian, J Rueckel, BF Hoppe, ... JAMA Network Open 4 (12), e2141096-e2141096, 2021 | 70 | 2021 |
| 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 | 67 | 2016 |
| 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 | 64 | 2018 |
| Detection of breast cancer from whole slide histopathological images using deep multiple instance CNN K Das, S Conjeti, J Chatterjee, D Sheet IEEE Access 8, 213502-213511, 2020 | 63 | 2020 |
| Complex fully convolutional neural networks for MR image reconstruction MA Dedmari, S Conjeti, S Estrada, P Ehses, T Stöcker, M Reuter International Workshop on Machine Learning for Medical Image Reconstruction …, 2018 | 63 | 2018 |
| 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 | 48 | 2011 |
| 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, 302-307, 2016 | 46 | 2016 |
Nassir NavabProfessor of Computer Science, Technische Universität München在 cs.tum.edu 的电子邮件经过验证
