| Cluster failure: Why fMRI inferences for spatial extent have inflated false positive rates A Eklund, TE Nichols, H Knutsson Proceedings of the National Academy of Sciences 113 (28), 7900–7905, 2016 | 2710 | 2016 |
| Medical image processing on the GPU–Past, present and future A Eklund, P Dufort, D Forsberg, SM LaConte Medical image analysis 17 (8), 1073-1094, 2013 | 424 | 2013 |
| BIDS Apps: Improving ease of use, accessibility and reproducibility of neuroimaging data analysis methods K Gorgolewski, F Alfaro-Almagro, T Auer, P Bellec, M Capota, ... PLOS Computational Biology 13 (3), e1005209, 2017 | 146 | 2017 |
| Does parametric fMRI analysis with SPM yield valid results? - An empirical study of 1484 rest datasets A Eklund, M Andersson, C Josephson, M Johannesson, H Knutsson NeuroImage 61 (3), 565-578, 2012 | 124 | 2012 |
| BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs A Eklund, P Dufort, M Villani, S LaConte Frontiers in Neuroinformatics 8, 24, 2014 | 82 | 2014 |
| Can parametric statistical methods be trusted for fMRI based group studies? A Eklund, T Nichols, H Knutsson arXiv:1511.01863, 2015 | 66 | 2015 |
| fMRI analysis on the GPU—possibilities and challenges A Eklund, M Andersson, H Knutsson Computer methods and programs in biomedicine 105 (2), 145-161, 2012 | 63 | 2012 |
| Generative adversarial networks for image-to-image translation on multi-contrast MR images - A comparison of CycleGAN and UNIT P Welander, S Karlsson, A Eklund arXiv:1806.07777, 2018 | 53 | 2018 |
| Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates A Eklund, H Knutsson, TE Nichols Human Brain Mapping 40, 2017-2032, 2019 | 41 | 2019 |
| Phase based volume registration using CUDA A Eklund, M Andersson, H Knutsson International Conference on Acoustics, Speech and Signal Processing (ICASSP …, 2010 | 34 | 2010 |
| Fast Bayesian whole-brain fMRI analysis with spatial 3D priors P Sidén, A Eklund, D Bolin, M Villani NeuroImage 146, 211-225, 2017 | 32 | 2017 |
| Fast random permutation tests enable objective evaluation of methods for single-subject FMRI analysis A Eklund, M Andersson, H Knutsson International journal of biomedical imaging 2011, 2011 | 31 | 2011 |
| True 4D image denoising on the GPU A Eklund, M Andersson, H Knutsson International Journal of Biomedical Imaging 2011, 8, 2011 | 29 | 2011 |
| Using real-time fMRI to control a dynamical system by brain activity classification A Eklund, H Ohlsson, M Andersson, J Rydell, A Ynnerman, H Knutsson MICCAI, 1000-1008, 2009 | 28 | 2009 |
| Reply to Brown and Behrmann, Cox et al. and Kessler et al.: Data and code sharing is the way forward for fMRI A Eklund, T Nichols, H Knutsson Proceedings of the National Academy of Sciences 114, E3374–E3375, 2017 | 25 | 2017 |
| Classification of short time series in early Parkinson's disease with deep learning of fuzzy recurrence plots T Pham, K Wårdell, A Eklund, G Salerud IEEE/CAA Journal of Automatica Sinica 6, 1306-1317, 2019 | 24 | 2019 |
| Refacing: reconstructing anonymized facial features using GANs D Abramian, A Eklund IEEE International Symposium on Biomedical Imaging (ISBI), 1104-1108, 2019 | 22 | 2019 |
| Empirically investigating the statistical validity of SPM, FSL and AFNI for single subject fMRI analysis A Eklund, T Nichols, M Andersson, H Knutsson IEEE International Symposium on Biomedical Imaging (ISBI), 1376-1380, 2015 | 22 | 2015 |
| A GPU accelerated interactive interface for exploratory functional connectivity analysis of fMRI data A Eklund, O Friman, M Andersson, H Knutsson IEEE International Conference on Image Processing, 1589-1592, 2011 | 22 | 2011 |
| Phase-based non-rigid 3D image registration-from minutes to seconds using CUDA D Forsberg, A Eklund, M Andersson, H Knutsson Joint MICCAI Workshop on High Performance and Distributed Computing for …, 2011 | 22 | 2011 |
Hans KnutssonProfessor of medical informaticsVerified email at liu.se
