| Opposition-based learning: a new scheme for machine intelligence HR Tizhoosh International Conference on Computational Intelligence for Modelling …, 2005 | 2515 | 2005 |
| Opposition-based differential evolution S Rahnamayan, HR Tizhoosh, MMA Salama IEEE Transactions on Evolutionary computation 12 (1), 64-79, 2008 | 1925 | 2008 |
| Artificial Intelligence and Digital Pathology: Challenges and Opportunities HR Tizhoosh, L Pantanowitz Journal of Pathology Informatics 9 (38), 2018 | 517 | 2018 |
| Quasi-oppositional differential evolution S Rahnamayan, HR Tizhoosh, MMA Salama 2007 IEEE congress on evolutionary computation, 2229-2236, 2007 | 445 | 2007 |
| A novel population initialization method for accelerating evolutionary algorithms S Rahnamayan, HR Tizhoosh, MMA Salama Computers & Mathematics with Applications 53 (10), 1605-1614, 2007 | 442 | 2007 |
| Opposition versus randomness in soft computing techniques S Rahnamayan, HR Tizhoosh, MMA Salama Applied Soft Computing 8 (2), 906-918, 2008 | 400 | 2008 |
| Image thresholding using type II fuzzy sets HR Tizhoosh Pattern recognition 38 (12), 2363-2372, 2005 | 380 | 2005 |
| Fuzzy image processing HR Tizhoosh Publisher: Springer-Verlag. Kartoniert (TB), Deutsch 10, 1997 | 275* | 1997 |
| Opposition-based differential evolution algorithms S Rahnamayan, HR Tizhoosh, MMA Salama 2006 IEEE international conference on evolutionary computation, 2010-2017, 2006 | 247 | 2006 |
| Opposition-based reinforcement learning HR Tizhoosh Journal of Advanced Computational Intelligence and Intelligent Informatics …, 2006 | 241 | 2006 |
| Federated learning and differential privacy for medical image analysis M Adnan, S Kalra, JC Cresswell, GW Taylor, HR Tizhoosh Scientific reports 12 (1), 1953, 2022 | 235 | 2022 |
| Fuzzy image processing: an overview HR Tizhoosh, H Haußecker Handbook on Computer Vision and Applications 2, 683-727, 1999 | 229* | 1999 |
| Convolutional neural networks for histopathology image classification: Training vs. using pre-trained networks B Kieffer, M Babaie, S Kalra, HR Tizhoosh 2017 seventh international conference on image processing theory, tools and …, 2017 | 183 | 2017 |
| Reinforcement learning based on actions and opposite actions HR Tizhoosh International conference on artificial intelligence and machine learning 414, 2005 | 164 | 2005 |
| Improving the convergence of backpropagation by opposite transfer functions M Ventresca, HR Tizhoosh The 2006 IEEE International Joint Conference on Neural Network Proceedings …, 2006 | 156 | 2006 |
| Opposition-based differential evolution for optimization of noisy problems S Rahnamayan, HR Tizhoosh, MMA Salama 2006 IEEE International Conference on Evolutionary Computation, 1865-1872, 2006 | 151 | 2006 |
| A comparative study of CNN, BoVW and LBP for classification of histopathological images MD Kumar, M Babaie, S Zhu, S Kalra, HR Tizhoosh 2017 IEEE symposium series on computational intelligence (SSCI), 1-7, 2017 | 140 | 2017 |
| Ignorance functions. An application to the calculation of the threshold in prostate ultrasound images H Bustince, M Pagola, E Barrenechea, J Fernández, P Melo-Pinto, ... Fuzzy sets and Systems 161 (1), 20-36, 2010 | 132 | 2010 |
| Fine-tuning and training of densenet for histopathology image representation using tcga diagnostic slides A Riasatian, M Babaie, D Maleki, S Kalra, M Valipour, S Hemati, M Zaveri, ... Medical image analysis 70, 102032, 2021 | 131 | 2021 |
| Fast fuzzy edge detection HR Tizhoosh 2002 Annual Meeting of the North American Fuzzy Information Processing …, 2002 | 118 | 2002 |
