Information theoretic learning: Renyi's entropy and kernel perspectives JC Principe Springer Science & Business Media, 2010 | 1870 | 2010 |
Tversky loss function for image segmentation using 3D fully convolutional deep networks SSM Salehi, D Erdogmus, A Gholipour International workshop on machine learning in medical imaging, 379-387, 2017 | 977 | 2017 |
Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks JM Brown, JP Campbell, A Beers, K Chang, S Ostmo, RVP Chan, J Dy, ... JAMA ophthalmology 136 (7), 803-810, 2018 | 529 | 2018 |
The future of human-in-the-loop cyber-physical systems G Schirner, D Erdogmus, K Chowdhury, T Padir Computer 46 (1), 36-45, 2013 | 466 | 2013 |
An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems D Erdogmus, JC Principe IEEE Transactions on Signal Processing 50 (7), 1780-1786, 2002 | 412 | 2002 |
Generalized information potential criterion for adaptive system training D Erdogmus, JC Principe IEEE Transactions on Neural Networks 13 (5), 1035-1044, 2002 | 281 | 2002 |
Locally defined principal curves and surfaces U Ozertem, D Erdogmus The Journal of Machine Learning Research 12, 1249-1286, 2011 | 255 | 2011 |
Optimizing the P300-based brain–computer interface: current status, limitations and future directions JN Mak, Y Arbel, JW Minett, LM McCane, B Yuksel, D Ryan, D Thompson, ... Journal of neural engineering 8 (2), 025003, 2011 | 252 | 2011 |
Auto-context convolutional neural network (auto-net) for brain extraction in magnetic resonance imaging SSM Salehi, D Erdogmus, A Gholipour IEEE transactions on medical imaging 36 (11), 2319-2330, 2017 | 249 | 2017 |
Blind source separation using Renyi's mutual information KE Hild, D Erdogmus, J Príncipe IEEE Signal Processing Letters 8 (6), 174-176, 2001 | 210 | 2001 |
Feature extraction using information-theoretic learning KE Hild, D Erdogmus, K Torkkola, JC Principe IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (9), 1385-1392, 2006 | 208 | 2006 |
Asymmetric loss functions and deep densely-connected networks for highly-imbalanced medical image segmentation: Application to multiple sclerosis lesion detection SR Hashemi, SSM Salehi, D Erdogmus, SP Prabhu, SK Warfield, ... IEEE Access 7, 1721-1735, 2018 | 193 | 2018 |
Structured adversarial attack: Towards general implementation and better interpretability K Xu, S Liu, P Zhao, PY Chen, H Zhang, Q Fan, D Erdogmus, Y Wang, ... arXiv preprint arXiv:1808.01664, 2018 | 182 | 2018 |
Quantitative change of EEG and respiration signals during mindfulness meditation A Ahani, H Wahbeh, H Nezamfar, M Miller, D Erdogmus, B Oken Journal of neuroengineering and rehabilitation 11, 1-11, 2014 | 181 | 2014 |
A comparison of optimal MIMO linear and nonlinear models for brain–machine interfaces SP Kim, JC Sanchez, YN Rao, D Erdogmus, JM Carmena, MA Lebedev, ... Journal of neural engineering 3 (2), 145, 2006 | 157 | 2006 |
Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity TK Redd, JP Campbell, JM Brown, SJ Kim, S Ostmo, RVP Chan, J Dy, ... British Journal of Ophthalmology 103 (5), 580-584, 2019 | 151 | 2019 |
The Cauchy–Schwarz divergence and Parzen windowing: Connections to graph theory and Mercer kernels R Jenssen, JC Principe, D Erdogmus, T Eltoft Journal of the Franklin Institute 343 (6), 614-629, 2006 | 150 | 2006 |
A novel LMS algorithm applied to adaptive noise cancellation JM Górriz, J Ramírez, S Cruces-Alvarez, CG Puntonet, EW Lang, ... IEEE Signal Processing Letters 16 (1), 34-37, 2008 | 149 | 2008 |
Clustering using Renyi's entropy R Jenssen, KE Hild, D Erdogmus, JC Principe, T Eltoft Proceedings of the International Joint Conference on Neural Networks, 2003 …, 2003 | 146 | 2003 |
SNR-optimality of sum-of-squares reconstruction for phased-array magnetic resonance imaging EG Larsson, D Erdogmus, R Yan, JC Principe, JR Fitzsimmons Journal of Magnetic Resonance 163 (1), 121-123, 2003 | 145 | 2003 |