Sergey M. Plis | DeepAI

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∙ 07/14/2023

Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey

Deep learning (DL) models have been popular due to their ability to lear...

0 Md Mahfuzur Rahman, et al. ∙

∙ 09/07/2022

Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes

Recent neuroimaging studies that focus on predicting brain disorders via...

2 Alex Fedorov, et al. ∙

∙ 05/17/2021

Algorithm-Agnostic Explainability for Unsupervised Clustering

Supervised machine learning explainability has greatly expanded in recen...

0 Charles A. Ellis, et al. ∙

∙ 03/29/2021

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

Self-supervised learning has enabled significant improvements on natural...

25 Alex Fedorov, et al. ∙

∙ 02/18/2021

Efficient Distributed Auto-Differentiation

Although distributed machine learning has opened up numerous frontiers o...

0 Bradley T. Baker, et al. ∙

∙ 12/25/2020

Taxonomy of multimodal self-supervised representation learning

Sensory input from multiple sources is crucial for robust and coherent h...

12 Alex Fedorov, et al. ∙

∙ 12/25/2020

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

Introspection of deep supervised predictive models trained on functional...

20 Alex Fedorov, et al. ∙

∙ 07/29/2020

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

Behavioral changes are the earliest signs of a mental disorder, but argu...

0 Usman Mahmood, et al. ∙

∙ 11/11/2019

Multidataset Independent Subspace Analysis with Application to Multimodal Fusion

In the last two decades, unsupervised latent variable models—blind sourc...

21 Rogers F. Silva, et al. ∙

∙ 10/28/2019

Improved Differentially Private Decentralized Source Separation for fMRI Data

Blind source separation algorithms such as independent component analysi...

0 Hafiz Imtiaz, et al. ∙

∙ 11/18/2017

Run, skeleton, run: skeletal model in a physics-based simulation

In this paper, we present our approach to solve a physics-based reinforc...

0 Mikhail Pavlov, et al. ∙

∙ 11/03/2016

Recurrent Neural Networks for Spatiotemporal Dynamics of Intrinsic Networks from fMRI Data

Functional magnetic resonance imaging (fMRI) of temporally-coherent bloo...

0 R Devon Hjelm, et al. ∙

∙ 03/21/2016

Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging Data

Independent component analysis (ICA), as an approach to the blind source...

0 R Devon Hjelm, et al. ∙

∙ 12/20/2013

Deep learning for neuroimaging: a validation study

Deep learning methods have recently made notable advances in the tasks o...

0 Sergey M. Plis, et al. ∙

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