Eklund, Anders

Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates

Eklund, A., Nichols, T. E., & Knutsson, H. (2016) · Proceedings of the National Academy of Sciences

Functional Brain Connectivity StudiesAdvanced MRI Techniques and Applications

Cited by 3,487

BIDS apps: improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

Gorgolewski, K. J., Alfaro-Almagro, F., Auer, T., Bellec, P., Capotă, M., Chakravarty, M. M., Churchill, N. W., Cohen, A. L., Craddock, R. C., Devenyi, G. A., et al. (2017) · PLOS Computational Biology

Scientific Computing and Data ManagementResearch Data Management Practices

Cited by 311

Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates

Eklund, A., Knutsson, H., & Nichols, T. E. (2019) · Human Brain Mapping

Functional Brain Connectivity StudiesAdvanced MRI Techniques and Applications

Cited by 82

Refacing: Reconstructing Anonymized Facial Features Using GANS

Abramian, D., & Eklund, A. (2019) · 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

Generative Adversarial Networks and Image SynthesisDigital Media Forensic Detection

Cited by 63

Fusion of electronic health records and radiographic images for a multimodal deep learning prediction model of atypical femur fractures

Schilcher, J., Nilsson, A., Andlid, O., & Eklund, A. (2024) · Computers in Biology and Medicine

Bone health and osteoporosis researchArtificial Intelligence in Healthcare and Education

Cited by 41

Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images

Tampu, I. E., Eklund, A., & Haj-Hosseini, N. (2022) · Scientific Data

Retinal Imaging and AnalysisMachine Learning and Data Classification

Cited by 1