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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...
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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...
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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...
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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...
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01/01/2022
Dynamic Persistent Homology for Brain Networks via Wasserstein Graph Clustering
We present the novel Wasserstein graph clustering for dynamically changi...
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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...
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02/18/2021
Efficient Distributed Auto-Differentiation
Although distributed machine learning has opened up numerous frontiers o...
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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 ...
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12/25/2020
Taxonomy of multimodal self-supervised representation learning
Sensory input from multiple sources is crucial for robust and coherent h...
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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...
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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...
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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...
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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...
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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...
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06/16/2020
Interpretable multimodal fusion networks reveal mechanisms of brain cognition
Multimodal fusion benefits disease diagnosis by providing a more compreh...
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01/22/2020
Causality based Feature Fusion for Brain Neuro-Developmental Analysis
Human brain development is a complex and dynamic process that is affecte...
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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...
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11/11/2019
Multidataset Independent Subspace Analysis with Application to Multimodal Fusion
In the last two decades, unsupervised latent variable models—blind sourc...
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04/24/2019
Prediction of Progression to Alzheimer`s disease with Deep InfoMax
Arguably, unsupervised learning plays a crucial role in the majority of ...
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12/20/2013