Calheiros, A.J.P.[Alan J. P.]
* 2022: Impact of Multi-Thresholds and Vector Correction for Tracking Precipitating Systems over the Amazon Basin
Calhoun, C.[Claire]
* 2024: Geometric Correspondence-Based Multimodal Learning for Ophthalmic Image Analysis
Calhoun, G.[Gloria]
* 2019: Visualizations for Communicating Intelligent Agent Generated Courses of Action
Calhoun, R.[Ronald]
* 2018: Wind Gust Detection and Impact Prediction for Wind Turbines
Calhoun, V.[Vince]
* 2018: FDR-Corrected Sparse Canonical Correlation Analysis With Applications to Imaging Genomics
* 2020: Optimized Combination of Multiple Graphs With Application to the Integration of Brain Imaging and (epi)Genomics Data
* 2021: Joint Analysis of Multi-Paradigm fMRI Data With Its Application to Cognitive Study, A
* 2022: Deep Learning From Imaging Genetics for Schizophrenia Classification
* 2022: Explainable AI (XAI) In Biomedical Signal and Image Processing: Promises and Challenges
Includes: Calhoun, V.[Vince] Calhoun, V.
Calhoun, V.D.
* 2005: Bayesian Blind Source Separation for Brain Imaging
* 2008: Does the Brain Rest?: An Independent Component Analysis of Temporally Coherent Brain Networks at Rest and During a Cognitive Task
* 2008: Identification of Brain Image Biomarkers by Optimized Selection of Multimodal Independent Components
* 2008: Method to Analyze Correlations between Multiple Brain Imaging Tasks to Characterize Schizophrenia, A
* 2008: Parallel Independent Component Analysis Approach to Investigate Genomic Influence on Brain Function, A
* 2008: Sparse shift-invariant NMF
* 2012: De-noising, phase ambiguity correction and visualization techniques for complex-valued ICA of group fMRI data
* 2013: Guest Editorial for Special Section on Multimodal Biomedical Imaging: Algorithms and Applications
* 2014: Multidataset independent subspace analysis extends independent vector analysis
* 2014: Performance of complex-valued ICA algorithms for fMRI analysis: Importance of taking full diversity into account
* 2015: Multimodal Data Fusion Using Source Separation: Application to Medical Imaging
* 2015: Multimodal Data Fusion Using Source Separation: Two Effective Models Based on ICA and IVA and Their Properties
* 2015: Sensory load hierarchy-based classification of schizophrenia patients
* 2016: Cross-Frequency rs-fMRI Network Connectivity Patterns Manifest Differently for Schizophrenia Patients and Healthy Controls
* 2016: Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia
* 2016: Multimodal fusion of brain structural and functional imaging with a deep neural machine translation approach
* 2016: Time-Varying Brain Connectivity in fMRI Data: Whole-brain data-driven approaches for capturing and characterizing dynamic states
* 2017: Quantifying the Interaction and Contribution of Multiple Datasets in Fusion: Application to the Detection of Schizophrenia
* 2018: Enforcing Co-Expression Within a Brain-Imaging Genomics Regression Framework
* 2018: Estimation of Dynamic Sparse Connectivity Patterns From Resting State fMRI
* 2018: Fast and Accurate Detection of Complex Imaging Genetics Associations Based on Greedy Projected Distance Correlation
* 2018: Fused Estimation of Sparse Connectivity Patterns From Rest fMRI: Application to Comparison of Children and Adult Brains
* 2018: Graph Modularity and Randomness Measures: A Comparative Study
* 2018: In-between and cross-frequency dependence-based summarization of resting-state fMRI data
* 2018: In-between and cross-frequency dependence-based summarization of resting-state fMRI data
* 2018: Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia
* 2019: Extraction of Time-Varying Spatiotemporal Networks Using Parameter-Tuned Constrained IVA
* 2019: Translational Potential of Neuroimaging Genomic Analyses to Diagnosis and Treatment in Mental Disorders
* 2020: Causality-Based Feature Fusion for Brain Neuro-Developmental Analysis
* 2020: Estimating Dynamic Functional Brain Connectivity With a Sparse Hidden Markov Model
* 2020: Joint Bayesian-Incorporating Estimation of Multiple Gaussian Graphical Models to Study Brain Connectivity Development in Adolescence
* 2020: Machine Learning Model for Exploring Aberrant Functional Network Connectivity Transition in Schizophrenia, A
* 2020: Multi-Hypergraph Learning-Based Brain Functional Connectivity Analysis in fMRI Data
* 2020: Nicotine Addiction Decreases Dynamic Connectivity Frequency In Functional Magnetic Resonance Imaging
* 2020: Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data With a Phase Sparsity Constraint
* 2020: Transient Spectral Peak Analysis Reveals Distinct Temporal Activation Profiles for Different Functional Brain Networks
* 2021: Interpretable Multimodal Fusion Networks Reveal Mechanisms of Brain Cognition
* 2021: Multidataset Independent Subspace Analysis With Application to Multimodal Fusion
* 2022: Deep Learning in Neuroimaging: Promises and challenges
* 2022: Low-Rank Tucker-2 Model for Multi-Subject fMRI Data Decomposition With Spatial Sparsity Constraint
* 2022: Multi-Modal Imaging Genetics Data Fusion via a Hypergraph-Based Manifold Regularization: Application to Schizophrenia Study
* 2024: Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks
Includes: Calhoun, V.D. Calhoun, V.D.[Vince D.]
42 for Calhoun, V.D.
Calhoun, Z.D.[Zachary D.]
* 2022: Self-Supervised Encoders Are Better Transfer Learners in Remote Sensing Applications