Search Results for author: Alex Fedorov

Found 9 papers, 4 papers with code

Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data

no code implementations29 Mar 2021 Alex Fedorov, Eloy Geenjaar, Lei Wu, Thomas P. DeRamus, Vince D. Calhoun, Sergey M. Plis

We show that self-supervised models are not as robust as expected based on their results in natural imaging benchmarks and can be outperformed by supervised learning with dropout.

Out-of-Distribution Generalization Self-Supervised Learning

On self-supervised multi-modal representation learning: An application to Alzheimer's disease

no code implementations25 Dec 2020 Alex Fedorov, Lei Wu, Tristan Sylvain, Margaux Luck, Thomas P. DeRamus, Dmitry Bleklov, Sergey M. Plis, Vince D. Calhoun

In this paper, we introduce a way to exhaustively consider multimodal architectures for contrastive self-supervised fusion of fMRI and MRI of AD patients and controls.

General Classification Representation Learning

Whole MILC: generalizing learned dynamics across tasks, datasets, and populations

1 code implementation29 Jul 2020 Usman Mahmood, Md Mahfuzur Rahman, Alex Fedorov, Noah Lewis, Zening Fu, Vince D. Calhoun, Sergey M. Plis

In this paper we present a novel self supervised training schema which reinforces whole sequence mutual information local to context (whole MILC).

Feature Importance

Transfer Learning of fMRI Dynamics

no code implementations16 Nov 2019 Usman Mahmood, Md Mahfuzur Rahman, Alex Fedorov, Zening Fu, Sergey Plis

In this paper, we demonstrate a self-supervised pre-training method that enables us to pre-train directly on fMRI dynamics of healthy control subjects and transfer the learning to much smaller datasets of schizophrenia.

Small Data Image Classification Transfer Learning

Prediction of Progression to Alzheimer's disease with Deep InfoMax

no code implementations24 Apr 2019 Alex Fedorov, R. Devon Hjelm, Anees Abrol, Zening Fu, Yuhui Du, Sergey Plis, Vince D. Calhoun

Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging.

General Classification

Almost instant brain atlas segmentation for large-scale studies

1 code implementation1 Nov 2017 Alex Fedorov, Eswar Damaraju, Vince Calhoun, Sergey Plis

Complexity of the task increases even further when segmenting structural MRI of the brain into an atlas with more than 50 regions.

End-to-end learning of brain tissue segmentation from imperfect labeling

1 code implementation3 Dec 2016 Alex Fedorov, Jeremy Johnson, Eswar Damaraju, Alexei Ozerin, Vince Calhoun, Sergey Plis

Segmenting a structural magnetic resonance imaging (MRI) scan is an important pre-processing step for analytic procedures and subsequent inferences about longitudinal tissue changes.

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