| Learning deep representations by mutual information estimation and maximization RD Hjelm, A Fedorov, S Lavoie-Marchildon, K Grewal, P Bachman, ... The International Conference on Learning Representations (ICLR) 2019, 2018 | 3150 | 2018 |
| Deep attention recurrent Q-network I Sorokin, A Seleznev, M Pavlov, A Fedorov, A Ignateva Deep Reinforcement Learning Workshop, NIPS 2015, 2015 | 196 | 2015 |
| Deep residual learning for neuroimaging: An application to predict progression to alzheimer’s disease A Abrol, M Bhattarai, A Fedorov, Y Du, S Plis, V Calhoun, ... Journal of neuroscience methods 339, 108701, 2020 | 158 | 2020 |
| Group ICA for identifying biomarkers in schizophrenia:‘Adaptive’networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression MS Salman, Y Du, D Lin, Z Fu, A Fedorov, E Damaraju, J Sui, J Chen, ... NeuroImage: Clinical 22, 101747, 2019 | 105 | 2019 |
| End-to-end learning of brain tissue segmentation from imperfect labeling A Fedorov, J Johnson, E Damaraju, A Ozerin, V Calhoun, S Plis 2017 International Joint Conference on Neural Networks (IJCNN), 3785-3792, 2017 | 52 | 2017 |
| Prediction of Progression to Alzheimer's disease with Deep InfoMax A Fedorov, RD Hjelm, A Abrol, Z Fu, Y Du, S Plis, VD Calhoun 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2019 | 30 | 2019 |
| On Self-Supervised Multimodal Representation Learning: An Application To Alzheimer’s Disease A Fedorov, L Wu, T Sylvain, M Luck, TP DeRamus, D Bleklov, SM Plis, ... 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 1548-1552, 2021 | 24 | 2021 |
| Whole MILC: generalizing learned dynamics across tasks, datasets, and populations U Mahmood, MM Rahman, A Fedorov, N Lewis, Z Fu, VD Calhoun, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 22 | 2020 |
| Interpreting models interpreting brain dynamics M Rahman, U Mahmood, N Lewis, H Gazula, A Fedorov, Z Fu, ... Scientific Reports 12 (1), 1-15, 2022 | 17 | 2022 |
| Almost instant brain atlas segmentation for large-scale studies A Fedorov, E Damaraju, V Calhoun, S Plis NeurIPS 2017 BigNeuro Workshop, 2017 | 15 | 2017 |
| Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer’s Disease A Fedorov, T Sylvain, E Geenjaar, M Luck, L Wu, TP DeRamus, A Kirilin, ... 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), 23-30, 2021 | 13* | 2021 |
| Transfer Learning of fMRI Dynamics U Mahmood, MM Rahman, A Fedorov, Z Fu, S Plis Machine Learning for Health (ML4H) at NeurIPS 2019, 2019 | 8 | 2019 |
| Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links A Fedorov, E Geenjaar, L Wu, T Sylvain, TP DeRamus, M Luck, M Misiura, ... NeuroImage 285, 120485, 2024 | 5 | 2024 |
| Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data A Fedorov, E Geenjaar, L Wu, TP DeRamus, VD Calhoun, SM Plis RobustML workshop paper at ICLR 2021, 2021 | 5 | 2021 |
| Learnt dynamics generalizes across tasks, datasets, and populations U Mahmood, MM Rahman, A Fedorov, Z Fu, VD Calhoun, SM Plis arXiv preprint arXiv:1912.03130, 2019 | 5 | 2019 |
| Chromatic fusion: Generative multimodal neuroimaging data fusion provides multi‐informed insights into schizophrenia EPT Geenjaar, NL Lewis, A Fedorov, L Wu, JM Ford, A Preda, SM Plis, ... Human Brain Mapping 44 (17), 5828-5845, 2023 | 4 | 2023 |
| Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes A Fedorov, E Geenjaar, L Wu, T Sylvain, TP DeRamus, M Luck, M Misiura, ... arXiv preprint arXiv:2209.02876, 2022 | 2 | 2022 |
| SQUWA: Signal Quality Aware DNN Architecture for Enhanced Accuracy in Atrial Fibrillation Detection from Noisy PPG Signals R Yan, C Ding, R Xiao, A Fedorov, RJ Lee, F Nahab, X Hu Conference on Health, Inference, and Learning (CHIL) 2024, 2024 | 1 | 2024 |
| Pipeline-Invariant Representation Learning for Neuroimaging X Li, A Fedorov, M Mathur, A Abrol, G Kiar, S Plis, V Calhoun Machine Learning for Health (ML4H) Symposium 2022 at NeurIPS 2022, 2022 | 1 | 2022 |
| Enabling Pre-Shock State Detection using Electrogram Signals from Implantable Cardioverter-Defibrillators R Yan, NK Bhatia, FM Merchant, A Fedorov, R Xiao, C Ding, X Hu Companion Proceedings of the ACM on Web Conference 2024, 1138-1141, 2024 | | 2024 |
Vince D. CalhounDirector-Translational Research in Neuroimaging and Data Science (TReNDS;GSU/GAtech/Emory)Verified email at gsu.edu