2024
Federated Privacy-Preserving Visualization: A Vision Paper
Tao Y, Sarwate A, Panta S, Plis S, Calhoun V. Federated Privacy-Preserving Visualization: A Vision Paper. 2024, 00: 8035-8041. DOI: 10.1109/bigdata62323.2024.10825849.Peer-Reviewed Original ResearchFederated learningRisk of data leakagePrivacy-preserving techniquesDifferential privacyData leakageSensitive informationVision paperFL applicationsModel trainingData visualizationExploratory data analysisCorrelation visualizationCentralized systemPrivacyLocal dataVisualizationDistribution dataData analysisMonitoring dataTaskDataLearningImplementationVisionInformationIntegrated Gradients Demystified: An MRI Case Study on Aβ-T Protein Localization
Dolci G, Morasso C, Cruciani F, Brusini L, Pini L, Calhoun V, Galazzo I, Menegaz G. Integrated Gradients Demystified: An MRI Case Study on Aβ-T Protein Localization. 2024, 00: 1177-1182. DOI: 10.1109/metroxraine62247.2024.10796298.Peer-Reviewed Original ResearchCGDM-GAN: An Adversarial Network Approach with Self-supervised Learning for Site Effect Removal
Cui X, Zhi D, Yan W, Calhoun V, Zhuo C, Sui J. CGDM-GAN: An Adversarial Network Approach with Self-supervised Learning for Site Effect Removal. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039732, DOI: 10.1109/embc53108.2024.10782176.Peer-Reviewed Original ResearchConceptsSelf-supervised learningIntrinsic image propertiesGeneralization of modelsSynthetic datasetsClassification performanceGenerative modelDiscrepancy minimizationImage dataNetwork approachDatasetData harmonizationImaging propertiesLearningNeuroimaging classificationCycleGANData harmonization methodsAdversaryABCD datasetAcquisition protocolsPerformanceEffective wayDataTaskCross-Sampling Rate Transfer Learning for Enhanced Raw EEG Deep Learning Classifier Performance in Major Depressive Disorder Diagnosis
Ellis C, Miller R, Calhoun V. Cross-Sampling Rate Transfer Learning for Enhanced Raw EEG Deep Learning Classifier Performance in Major Depressive Disorder Diagnosis. 2024, 00: 1-5. DOI: 10.1109/isbi56570.2024.10635743.Peer-Reviewed Original ResearchTransfer learningDeep learning classifier’s performanceEarly convolutional layersConvolutional neural networkDeep learning modelsDeep learning studiesConvolutional layersClassifier performanceDiagnosis tasksExplainability analysisNeural networkSleep datasetsRaw electroencephalographyLearning modelsIncreased robustnessDatasetChannel lossSampling rateModel accuracyMDD modelLearningRepresentationTaskLearning studiesElectroencephalographyMaximum Classifier Discrepancy Generative Adversarial Network for Jointly Harmonizing Scanner Effects and Improving Reproducibility of Downstream Tasks
Yan W, Fu Z, Jiang R, Sui J, Calhoun V. Maximum Classifier Discrepancy Generative Adversarial Network for Jointly Harmonizing Scanner Effects and Improving Reproducibility of Downstream Tasks. IEEE Transactions On Biomedical Engineering 2024, 71: 1170-1178. PMID: 38060365, PMCID: PMC11005005, DOI: 10.1109/tbme.2023.3330087.Peer-Reviewed Original ResearchDownstream tasksPerformance of downstream tasksOriginal feature spaceState-of-the-artAdversarial generative networkGAN generatorAdversarial networkFeature spaceOriginal imageGeneration networksClassification performanceSmall-sample problemTask objectivesGenerative modelImproved performanceTaskHarmony frameworkAnatomical layoutNetworkHarmonious methodsMulti-site collaborationSimulated dataLayoutScanner effectsDatasetIdentifying the Relationship Structure Among Multiple Datasets Using Independent Vector Analysis: Application to Multi-Task fMRI Data
Lehmann I, Hasija T, Gabrielson B, Akhonda M, Calhoun V, Adali T. Identifying the Relationship Structure Among Multiple Datasets Using Independent Vector Analysis: Application to Multi-Task fMRI Data. IEEE Access 2024, 12: 109443-109456. DOI: 10.1109/access.2024.3435526.Peer-Reviewed Original ResearchIndependent vector analysisTask datasetMultiple datasetsFeature extraction approachUser-defined thresholdsHigher-order statisticsMulti-task fMRI dataExtraction approachRelationship structureDatasetSimulation resultsHierarchical clusteringInterpretable componentsVector analysisFMRI-dataFMRI dataEffective wayMethodTaskDataActivated brain regionsHypothesis testingDistributional assumptionsInformation
2023
Constrained Independent Component Analysis Based on Entropy Bound Minimization for Subgroup Identification from Multi-subject fMRI Data
Yang H, Ghayem F, Gabrielson B, Akhonda M, Calhoun V, Adali T. Constrained Independent Component Analysis Based on Entropy Bound Minimization for Subgroup Identification from Multi-subject fMRI Data. 2023, 00: 1-5. DOI: 10.1109/icassp49357.2023.10095816.Peer-Reviewed Original ResearchIndependent vector analysisSynthetic dataConstrained independent component analysisEntropy bound minimizationComputational complexity limitationsDemixing matrixIndependent component analysisComputational costOrthogonality requirementData identificationAlgorithmFunctional networksNetworkComponent analysisDatasetFMRI dataComputerTaskEntropyOrthogonalitySubgroup identificationVector analysisBrain networksDensity modelCoupled CP Tensor Decomposition with Shared and Distinct Components for Multi-Task Fmri Data Fusion
Borsoi R, Lehmann I, Akhonda M, Calhoun V, Usevich K, Brie D, Adali T. Coupled CP Tensor Decomposition with Shared and Distinct Components for Multi-Task Fmri Data Fusion. 2023, 00: 1-5. DOI: 10.1109/icassp49357.2023.10096241.Peer-Reviewed Original ResearchCP tensor decompositionTensor factorization approachDataset-specific featuresTensor-based frameworkPost-processing stepExtract featuresFunctional magnetic resonance imagingHyperparameter selectionTensor decompositionData fusionMulti-taskingDiscover componentsMultiple datasetsTaskCoupling matrixFunctional magnetic resonance imaging dataHyperparametersDatasetFeaturesGroup differencesFactor approachDecompositionFusion
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