| Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets MA Schulz, BTT Yeo, JT Vogelstein, J Mourao-Miranada, JN Kather, ... Nature communications 11 (1), 1-15, 2020 | 240 | 2020 |
| Analysing humanly generated random number sequences: a pattern-based approach MA Schulz, B Schmalbach, P Brugger, K Witt PloS one 7 (7), e41531, 2012 | 53 | 2012 |
| Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research F Eitel, MA Schulz, M Seiler, H Walter, K Ritter Experimental Neurology 339, 113608, 2021 | 44 | 2021 |
| Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets MA Schulz, BTT Yeo, JT Vogelstein, J Mourao-Miranada, JN Kather, ... BioRxiv, 757054, 2019 | 35 | 2019 |
| Performance reserves in brain-imaging-based phenotype prediction MA Schulz, D Bzdok, S Haufe, JD Haynes, K Ritter Cell Reports 43 (1), 2024 | 33 | 2024 |
| Inferring disease subtypes from clusters in explanation space MA Schulz, M Chapman-Rounds, M Verma, D Bzdok, K Georgatzis Scientific Reports 10 (1), 12900, 2020 | 33 | 2020 |
| FIMAP: Feature importance by minimal adversarial perturbation M Chapman-Rounds, U Bhatt, E Pazos, MA Schulz, K Georgatzis Proceedings of the AAAI Conference on Artificial Intelligence 35 (13), 11433 …, 2021 | 18 | 2021 |
| A cognitive fingerprint in human random number generation MA Schulz, S Baier, B Timmermann, D Bzdok, K Witt Scientific reports 11 (1), 1-7, 2021 | 13 | 2021 |
| EMAP: Explanation by minimal adversarial perturbation M Chapman-Rounds, MA Schulz, E Pazos, K Georgatzis arXiv preprint arXiv:1912.00872, 2019 | 9 | 2019 |
| Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Nat Commun 11, 4238 MA Schulz, BTT Yeo, JT Vogelstein, J Mourao-Miranada, JN Kather, ... | 5 | 2020 |
| On utilizing uncertainty information in template‐based EEG‐fMRI ballistocardiogram artifact removal MA Schulz, C Regenbogen, C Moessnang, I Neuner, A Finkelmeyer, ... Psychophysiology 52 (6), 857-863, 2015 | 4 | 2015 |
| Emerging shifts in neuroimaging data analysis in the era of “big data” D Bzdok, MA Schulz, M Lindquist Personalized psychiatry: big data analytics in mental health, 99-118, 2019 | 3 | 2019 |
| Similar neural pathways link psychological stress and brain-age in health and multiple sclerosis MA Schulz, S Hetzer, F Eitel, S Asseyer, L Meyer-Arndt, T Schmitz-Hübsch, ... Iscience 26 (9), 2023 | 2* | 2023 |
| Data augmentation via partial nonlinear registration for brain-age prediction MA Schulz, A Koch, VE Guarino, D Kainmueller, K Ritter International Workshop on Machine Learning in Clinical Neuroimaging, 169-178, 2022 | 1 | 2022 |
| Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction MA Schulz, B Thirion, A Gramfort, G Varoquaux, D Bzdok arXiv preprint arXiv:2110.06135, 2021 | 1 | 2021 |
| DeepRepViz: Identifying Potential Confounders in Deep Learning Model Predictions RP Rane, JH Kim, A Umesha, D Stark, MA Schulz, K Ritter International Conference on Medical Image Computing and Computer-Assisted …, 2024 | | 2024 |
| TLIMB-a transfer learning framework for image analysis of the brain MA Schulz, JP Albrecht, A Yilmaz, A Koch, D Kainmüller, U Leser, K Ritter CEUR Workshop Proceedings, 1-6, 2024 | | 2024 |
| Inferring disease subtypes from clusters in explanation space (vol 14, 6223, 2024) MA Schulz, M Chapman-Rounds, M Verma, D Bzdok, K Georgatzis SCIENTIFIC REPORTS 14 (1), 2024 | | 2024 |
| Do transformers and CNNs learn different concepts of brain age? NT Siegel, D Kainmueller, F Deniz, K Ritter, MA Schulz bioRxiv, 2024.08. 09.607321, 2024 | | 2024 |
| Machine Learning in Neuroimaging Psychiatry: Scaling Behavior, Constraints, and Limitations MA Schulz Technische Universität Berlin, 2024 | | 2024 |
Danilo BzdokCanada CIFAR AI Chair at McGill University & Mila Quebec AI Institute, MontrealVerified email at mcgill.ca
