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Alex Fedorov
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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
28232018
Deep attention recurrent Q-network
I Sorokin, A Seleznev, M Pavlov, A Fedorov, A Ignateva
Deep Reinforcement Learning Workshop, NIPS 2015, 2015
1852015
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
1332020
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
932019
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
482017
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
312019
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
232021
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
202020
Interpreting models interpreting brain dynamics
M Rahman, U Mahmood, N Lewis, H Gazula, A Fedorov, Z Fu, ...
Scientific Reports 12 (1), 1-15, 2022
152022
Almost instant brain atlas segmentation for large-scale studies
A Fedorov, E Damaraju, V Calhoun, S Plis
NeurIPS 2017 BigNeuro Workshop, 2017
152017
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
12*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
92019
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
52021
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
52019
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
22023
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
22022
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
12024
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
12022
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
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
2024
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