Data-driven multimodal fusion: approaches and applications in psychiatric research
In the era of big data, where vast amounts of information are being generated and collected
at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal …
at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal …
Multimodal Fusion of Brain Imaging Data: Methods and Applications
Neuroimaging data typically include multiple modalities, such as structural or functional
magnetic resonance imaging, diffusion tensor imaging, and positron emission tomography …
magnetic resonance imaging, diffusion tensor imaging, and positron emission tomography …
MMIF-INet: Multimodal medical image fusion by invertible network
D He, W Li, G Wang, Y Huang, S Liu - Information Fusion, 2025 - Elsevier
Multimodal medical image fusion (MMIF) technology aims to generate fused images that
comprehensively reflect the information of tissues, organs, and metabolism, thereby …
comprehensively reflect the information of tissues, organs, and metabolism, thereby …
A scalable approach to independent vector analysis by shared subspace separation for multi-subject fMRI analysis
Joint blind source separation (JBSS) has wide applications in modeling latent structures
across multiple related datasets. However, JBSS is computationally prohibitive with high …
across multiple related datasets. However, JBSS is computationally prohibitive with high …
Association of neuroimaging data with behavioral variables: a class of multivariate methods and their comparison using multi-task fMRI data
It is becoming increasingly common to collect multiple related neuroimaging datasets either
from different modalities or from different tasks and conditions. In addition, we have non …
from different modalities or from different tasks and conditions. In addition, we have non …
Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms
Coupled tensor decompositions (CTDs) perform data fusion by linking factors from different
datasets. Although many CTDs have been already proposed, current works do not address …
datasets. Although many CTDs have been already proposed, current works do not address …
Flexible Multisubject Multiset FMRI Data Analysis Using Robust Discriminative Dictionary Learning
A novel dictionary learning method is proposed for analyzing multi-subject multiset
functional magnetic resonance imaging (fMRI) data. It is assumed that the subjects are …
functional magnetic resonance imaging (fMRI) data. It is assumed that the subjects are …
Coupled CP tensor decomposition with shared and distinct components for multi-task Fmri data fusion
Discovering components that are shared in multiple datasets, next to dataset-specific
features, has great potential for studying the relationships between different subjects or tasks …
features, has great potential for studying the relationships between different subjects or tasks …
Common and Distinct Subspace Analysis in Data Fusion: Application to the Fusion of Brain Imaging Data
MABS Akhonda - 2022 - search.proquest.com
Data-driven methods, such as those based on independent component analysis (ICA), make
very few assumptions on the data and the relationships of the datasets, and hence have …
very few assumptions on the data and the relationships of the datasets, and hence have …