Anders Eklund
Anders Eklund
Associate professor Biomedical Engineering, Linköping University
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
Vox2Vox: 3D GAN for brain tumour segmentation
MD Cirillo, D Abramian, A Eklund
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2020
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
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
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
Can parametric statistical methods be trusted for fMRI based group studies?
A Eklund, T Nichols, H Knutsson
arXiv:1511.01863, 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
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
Refacing: reconstructing anonymized facial features using GANs
D Abramian, A Eklund
IEEE International Symposium on Biomedical Imaging (ISBI), 1104-1108, 2019
Fast Bayesian whole-brain fMRI analysis with spatial 3D priors
P Sidén, A Eklund, D Bolin, M Villani
NeuroImage 146, 211-225, 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
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
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
What is the best data augmentation for 3D brain tumor segmentation?
MD Cirillo, D Abramian, A Eklund
IEEE International Conference on Image Processing, 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
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
Phase based volume registration using CUDA
A Eklund, M Andersson, H Knutsson
International Conference on Acoustics, Speech and Signal Processing (ICASSP …, 2010
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