Anders Eklund

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Hans KnutssonProfessor of medical informaticsVerified email at liu.se
Thomas E. NicholsUniversity of OxfordVerified email at bdi.ox.ac.uk
Mats AnderssonResearch engineer Linköping university SwedenVerified email at liu.se
David AbramianLinköping UniversityVerified email at liu.se
Mattias VillaniProfessor of Statistics, Stockholm UniversityVerified email at stat.su.se
Stephen LaConteVirginia TechVerified email at vtc.vt.edu
Iulian Emil TampuLinköping UniversityVerified email at liu.se
Daniel ForsbergAI Lead and Product Owner Radiology AI, Sectra Imaging IT Solutions ABVerified email at sectra.com
Per SidénQualcommVerified email at qti.qualcomm.com
Evren ÖzarslanDepartment of Biomedical Engineering, Linköping UniversityVerified email at liu.se
Muhammad Usman AkbarDepartment of Biomedical Engineering (IMT), Linköping University, Sweden.Verified email at liu.se
Marco Domenico Cirillo, Ph.D.Linköping UniversityVerified email at liu.se
Hamid BehjatLund UniversityVerified email at med.lu.se
Jens SjölundAssistant Professor at Uppsala UniversityVerified email at it.uu.se
Håkan OlaussonVerified email at liu.se
Carl-Fredrik WestinProfessor, Harvard Medical SchoolVerified email at bwh.harvard.edu
Måns LarssonEigenvision ABVerified email at chalmers.se
Markus NilssonAssociate professor, Department of Diagnostic Radiology, Lund UniversityVerified email at med.lu.se
Martin LindquistProfessor of Biostatistics, Johns Hopkins UniversityVerified email at jhsph.edu
Per NymanMD, Pediatrician, Pediatric oncologist, PhD-student Radiation PhysicsVerified email at regionostergotland.se

Anders Eklund

Associate professor Biomedical Engineering, Linköping University

Verified email at liu.se - Homepage

Medical imaging fMRI Statistics Machine learning Synthetic images


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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	3747	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	523	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	303	2017
Vox2Vox: 3D-GAN for brain tumour segmentation MD Cirillo, D Abramian, A Eklund Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2020	155	2020
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	143	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	135	2018
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	99	2014
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	78	2019
Can parametric statistical methods be trusted for fMRI based group studies? A Eklund, T Nichols, H Knutsson arXiv:1511.01863, 2015	75	2015
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	72	2019
Refacing: reconstructing anonymized facial features using GANs D Abramian, A Eklund IEEE International Symposium on Biomedical Imaging (ISBI), 1104-1108, 2019	66	2019
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
Fast Bayesian whole-brain fMRI analysis with spatial 3D priors P Sidén, A Eklund, D Bolin, M Villani NeuroImage 146, 211-225, 2017	49	2017
Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images IE Tampu, A Eklund, N Haj-Hosseini Scientific Data 9, 580, 2022	46	2022
What is the best data augmentation for 3D brain tumor segmentation? MD Cirillo, D Abramian, A Eklund IEEE International Conference on Image Processing, 2021	41	2021
Brainhack: developing a culture of open, inclusive, community-driven neuroscience R Gau, S Noble, K Heuer, KL Bottenhorn, IP Bilgin, YF Yang, ... Neuron 109, 1769-1775, 2021	39	2021
Generating diffusion MRI scalar maps from T1-weighted images using generative adversarial networks X Gu, H Knutsson, M Nilsson, A Eklund Scandinavian Conference on Image Analysis (SCIA), 489-498, 2019	39	2019
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	36	2011
Phase based volume registration using CUDA A Eklund, M Andersson, H Knutsson International Conference on Acoustics, Speech and Signal Processing (ICASSP …, 2010	31	2010
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	30	2009

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