| Underspecification presents challenges for credibility in modern machine learning A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ... Journal of Machine Learning Research 23 (226), 1-61, 2022 | 837 | 2022 |
| PRoNTo: pattern recognition for neuroimaging toolbox J Schrouff, MJ Rosa, JM Rondina, AF Marquand, C Chu, J Ashburner, ... Neuroinformatics 11, 319-337, 2013 | 494 | 2013 |
| Brain functional integration decreases during propofol-induced loss of consciousness J Schrouff, V Perlbarg, M Boly, G Marrelec, P Boveroux, ... Neuroimage 57 (1), 198-205, 2011 | 296 | 2011 |
| Biased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions Q Noirhomme, D Lesenfants, F Gomez, A Soddu, J Schrouff, G Garraux, ... NeuroImage: Clinical 4, 687-694, 2014 | 133 | 2014 |
| Intracranial electrophysiology reveals reproducible intrinsic functional connectivity within human brain networks A Kucyi, J Schrouff, S Bickel, BL Foster, JM Shine, J Parvizi Journal of Neuroscience 38 (17), 4230-4242, 2018 | 118 | 2018 |
| Diagnosing failures of fairness transfer across distribution shift in real-world medical settings J Schrouff, N Harris, OO Koyejo, I Alabdulmohsin, E Schnider, ... Advances in Neural Information Processing Systems, 2022 | 110* | 2022 |
| Cross-modal decoding of neural patterns associated with working memory: Evidence for attention-based accounts of working memory S Majerus, N Cowan, F Péters, L Van Calster, C Phillips, J Schrouff Cerebral Cortex 26 (1), 166-179, 2016 | 105 | 2016 |
| Multiclass classification of FDG PET scans for the distinction between Parkinson's disease and atypical parkinsonian syndromes G Garraux, C Phillips, J Schrouff, A Kreisler, C Lemaire, C Degueldre, ... NeuroImage: Clinical 2, 883-893, 2013 | 96 | 2013 |
| Mapping human temporal and parietal neuronal population activity and functional coupling during mathematical cognition AL Daitch, BL Foster, J Schrouff, V Rangarajan, I Kaşikçi, S Gattas, ... Proceedings of the National Academy of Sciences 113 (46), E7277-E7286, 2016 | 93 | 2016 |
| Healthsheet: development of a transparency artifact for health datasets N Rostamzadeh, D Mincu, S Roy, A Smart, L Wilcox, M Pushkarna, ... Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 74 | 2022 |
| Embedding anatomical or functional knowledge in whole-brain multiple kernel learning models J Schrouff, JM Monteiro, L Portugal, MJ Rosa, C Phillips, ... Neuroinformatics 16, 117-143, 2018 | 69 | 2018 |
| Detecting shortcut learning for fair medical AI using shortcut testing A Brown, N Tomasev, J Freyberg, Y Liu, A Karthikesalingam, J Schrouff Nature communications 14 (1), 4314, 2023 | 65 | 2023 |
| fMRI artefact rejection and sleep scoring toolbox Y Leclercq, J Schrouff, Q Noirhomme, P Maquet, C Phillips Computational intelligence and neuroscience 2011 (1), 598206, 2011 | 64 | 2011 |
| Experience‐dependent induction of hypnagogic images during daytime naps: A combined behavioural and EEG study C Kussé, A SHAFFII‐LE BOURDIEC, J Schrouff, L Matarazzo, P Maquet Journal of Sleep Research 21 (1), 10-20, 2012 | 53 | 2012 |
| Gender bias in (neuro) science: facts, consequences and solutions J Schrouff, D Pischedda, S Genon, G Fryns, AL Pinho, E Vassena, ... | 48 | 2019 |
| Correlation between resting state fMRI total neuronal activity and PET metabolism in healthy controls and patients with disorders of consciousness A Soddu, F Gómez, L Heine, C Di Perri, MA Bahri, HU Voss, MA Bruno, ... Brain and behavior 6 (1), e00424, 2016 | 45 | 2016 |
| Concurrent synaptic and systems memory consolidation during sleep L Mascetti, A Foret, J Schrouff, V Muto, V Dideberg, E Balteau, ... Journal of Neuroscience 33 (24), 10182-10190, 2013 | 44 | 2013 |
| Localizing and comparing weight maps generated from linear kernel machine learning models J Schrouff, J Cremers, G Garraux, L Baldassarre, J Mourão-Miranda, ... 2013 International Workshop on Pattern Recognition in Neuroimaging, 124-127, 2013 | 43 | 2013 |
| Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach LCL Portugal, J Schrouff, R Stiffler, M Bertocci, G Bebko, H Chase, ... NeuroImage: Clinical 23, 101813, 2019 | 41 | 2019 |
| Decoding intracranial EEG data with multiple kernel learning method J Schrouff, J Mourão-Miranda, C Phillips, J Parvizi Journal of neuroscience methods 261, 19-28, 2016 | 41 | 2016 |
Christophe PhillipsFRS-FNRS Research Director, University of LiègeVerifierad e-postadress på ulg.ac.be