Vince D. Calhoun | 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. ∙

∙ 03/21/2023

Higher-order Organization in the Human Brain from Matrix-Based Rényi's Entropy

Pairwise metrics are often employed to estimate statistical dependencies...

0 Qiang Li, et al. ∙

∙ 11/10/2022

New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity Using Dictionary Learning

Independent component analysis (ICA) of multi-subject functional magneti...

0 Fateme Ghayem, 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. ∙

∙ 01/01/2022

Dynamic Persistent Homology for Brain Networks via Wasserstein Graph Clustering

We present the novel Wasserstein graph clustering for dynamically changi...

0 Moo K. Chung, et al. ∙

∙ 05/17/2021

Algorithm-Agnostic Explainability for Unsupervised Clustering

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

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∙ 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. ∙

∙ 01/20/2021

Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction

Objective: Multi-modal functional magnetic resonance imaging (fMRI) can ...

14 Gang Qu, 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. ∙

∙ 10/25/2020

Detecting abnormal connectivity in schizophrenia via a joint directed acyclic graph estimation model

Functional connectivity (FC) has been widely used to study brain network...

0 Gemeng Zhang, et al. ∙

∙ 09/30/2020

Distance Correlation Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies

Resting-state functional magnetic resonance imaging (rs-fMRI)-derived fu...

11 Li Xiao, 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. ∙

∙ 06/16/2020

Causal inference of brain connectivity from fMRI with ψ-Learning Incorporated Linear non-Gaussian Acyclic Model (ψ-LiNGAM)

Functional connectivity (FC) has become a primary means of understanding...

0 Aiying Zhang, et al. ∙

∙ 06/16/2020

Interpretable multimodal fusion networks reveal mechanisms of brain cognition

Multimodal fusion benefits disease diagnosis by providing a more compreh...

0 Wenxing Hu, et al. ∙

∙ 01/22/2020

Causality based Feature Fusion for Brain Neuro-Developmental Analysis

Human brain development is a complex and dynamic process that is affecte...

12 Peyman Hosseinzadeh Kassani, et al. ∙

∙ 01/06/2020

Meta-modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia

Objective: Multimodal measurements of the same phenomena provide complem...

1 Haleh Falakshahi, 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. ∙

∙ 04/24/2019

Prediction of Progression to Alzheimer`s disease with Deep InfoMax

Arguably, unsupervised learning plays a crucial role in the majority of ...

0 Alex Fedorov, 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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