Blind fMRI source unmixing via higher-order tensor decompositions
C Chatzichristos, E Kofidis, M Morante… - Journal of neuroscience …, 2019 - Elsevier
Background The growing interest in neuroimaging technologies generates a massive
amount of biomedical data of high dimensionality. Tensor-based analysis of brain imaging …
amount of biomedical data of high dimensionality. Tensor-based analysis of brain imaging …
Dictionary learning-based fMRI data analysis for capturing common and individual neural activation maps
In this paper, a novel dictionary learning (DL) method is proposed to estimate sparse neural
activations from multi-subject fMRI data sets. By exploiting the label information such as the …
activations from multi-subject fMRI data sets. By exploiting the label information such as the …
Information assisted dictionary learning for fMRI data analysis
M Morante, Y Kopsinis, S Theodoridis… - IEEE …, 2020 - ieeexplore.ieee.org
In this paper, the task-related fMRI problem is treated in its matrix factorization form, focusing
on the Dictionary Learning (DL) approach. The proposed method allows the incorporation of …
on the Dictionary Learning (DL) approach. The proposed method allows the incorporation of …
The model order limit: deep sparse factorization for resting brain
The decomposition of resting state Functional Magnetic Resonance Imaging (rs-fMRI) data
by Dictionary Learning (DL) using sparsity constraint have been recently shown to be an …
by Dictionary Learning (DL) using sparsity constraint have been recently shown to be an …
Assisted dictionary learning for FMRI data analysis
MM Moreno, Y Kopsinis, E Kofidis… - … , Speech and Signal …, 2017 - ieeexplore.ieee.org
Extracting information from functional magnetic resonance images (fMRI) has been a major
area of research for more than two decades. The goal of this work is to present a new …
area of research for more than two decades. The goal of this work is to present a new …
Flexible large-scale fMRI analysis: A survey
Functional magnetic resonance imaging (fMRI) has provided a window into the brain with
wide adoption in research and even clinical settings. Data-driven methods such as those …
wide adoption in research and even clinical settings. Data-driven methods such as those …
Fast and robust fMRI unmixing using hierarchical dictionary learning
We propose a novel computationally efficient hierarchical dictionary learning (HDL)
approach for data-driven unmixing and functional connectivity analysis of functional …
approach for data-driven unmixing and functional connectivity analysis of functional …
Information assisted dictionary learning for fMRI data analysis
M Morante, Y Kopsinis, S Theodoridis… - arXiv preprint arXiv …, 2018 - arxiv.org
In this paper, the task-related fMRI problem is treated in its matrix factorization formulation,
focused on the Dictionary Learning (DL) approach. The new method allows the …
focused on the Dictionary Learning (DL) approach. The new method allows the …
[PDF][PDF] Dictionary Learning-Based fMRI Data Analysis for Capturing Common and Individual Neural Activation Maps
RJKK Dontaraju, SJ Kim, MAT Adali - ieeexplore.ieee.org
A novel dictionary learning (DL) method is proposed to estimate sparse neural activations
from multi-subject fMRI data sets. By exploiting the label information such as the patient and …
from multi-subject fMRI data sets. By exploiting the label information such as the patient and …
What is BackPropagation Examples Simple Example of a Neural Network Exercise of an Example with Python Exercise of an Example with Manuall Calculation
MLC Learning, CE Algo, SLRMN Zweig… - Machine …, 2020 - wwwlehre.dhbw-stuttgart.de
Mindjet Page 1 ML4-Decision Tree ML5-simple Linear R. & multiple Linear Regr. ML6-Neural
Networks: CNN ML2- Concept Learning: Version Spaces & CE Algo. Preparation for …
Networks: CNN ML2- Concept Learning: Version Spaces & CE Algo. Preparation for …