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09/15/2023
Cross-Modal Synthesis of Structural MRI and Functional Connectivity Networks via Conditional ViT-GANs
The cross-modal synthesis between structural magnetic resonance imaging ...
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05/18/2023
Learning low-dimensional dynamics from whole-brain data improves task capture
The neural dynamics underlying brain activity are critical to understand...
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04/15/2023
SalientGrads: Sparse Models for Communication Efficient and Data Aware Distributed Federated Training
Federated learning (FL) enables the training of a model leveraging decen...
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11/12/2022
MultiCrossViT: Multimodal Vision Transformer for Schizophrenia Prediction using Structural MRI and Functional Network Connectivity Data
Vision Transformer (ViT) is a pioneering deep learning framework that ca...
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10/07/2022
CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs
Communication within or between complex systems is commonplace in the na...
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09/15/2022
Prediction of Gender from Longitudinal MRI data via Deep Learning on Adolescent Data Reveals Unique Patterns Associated with Brain Structure and Change over a Two-year Period
Deep learning algorithms for predicting neuroimaging data have shown con...
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07/09/2022
Explainable AI (XAI) in Biomedical Signal and Image Processing: Promises and Challenges
Artificial intelligence has become pervasive across disciplines and fiel...
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05/26/2022
Spatio-temporally separable non-linear latent factor learning: an application to somatomotor cortex fMRI data
Functional magnetic resonance imaging (fMRI) data contain complex spatio...
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02/04/2022
Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series
Recently, methods that represent data as a graph, such as graph neural n...
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12/07/2021
A deep learning model for data-driven discovery of functional connectivity
Functional connectivity (FC) studies have demonstrated the overarching v...
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11/01/2021
Multi network InfoMax: A pre-training method involving graph convolutional networks
Discovering distinct features and their relations from data can help us ...
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11/01/2021
Brain dynamics via Cumulative Auto-Regressive Self-Attention
Multivariate dynamical processes can often be intuitively described by a...
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08/29/2021
Variational voxelwise rs-fMRI representation learning: Evaluation of sex, age, and neuropsychiatric signatures
We propose to apply non-linear representation learning to voxelwise rs-f...
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05/03/2021
Fusing multimodal neuroimaging data with a variational autoencoder
Neuroimaging studies often involve the collection of multiple data modal...
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10/28/2019
Improved Differentially Private Decentralized Source Separation for fMRI Data
Blind source separation algorithms such as independent component analysi...
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10/23/2019
Tracing Network Evolution Using the PARAFAC2 Model
Characterizing time-evolving networks is a challenging task, but it is c...
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04/04/2018
Improving Classification Rate of Schizophrenia Using a Multimodal Multi-Layer Perceptron Model with Structural and Functional MR
The wide variety of brain imaging technologies allows us to exploit info...
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11/01/2017
Almost instant brain atlas segmentation for large-scale studies
Large scale studies of group differences in healthy controls and patient...
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07/14/2017
Kernel Method for Detecting Higher Order Interactions in multi-view Data: An Application to Imaging, Genetics, and Epigenetics
In this study, we tested the interaction effect of multimodal datasets u...
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05/11/2017
FDR-Corrected Sparse Canonical Correlation Analysis with Applications to Imaging Genomics
Reducing the number of false positive discoveries is presently one of th...
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12/03/2016
End-to-end learning of brain tissue segmentation from imperfect labeling
Segmenting a structural magnetic resonance imaging (MRI) scan is an impo...
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11/03/2016
Recurrent Neural Networks for Spatiotemporal Dynamics of Intrinsic Networks from fMRI Data
Functional magnetic resonance imaging (fMRI) of temporally-coherent bloo...
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09/15/2016
Learning Schizophrenia Imaging Genetics Data Via Multiple Kernel Canonical Correlation Analysis
Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are empl...
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11/19/2015
