Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain
Yang L, Qiao C, Kanamori T, Calhoun V, Stephen J, Wilson T, Wang Y. Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain. Neural Networks 2024, 183: 106974. PMID: 39657530, PMCID: PMC12202986, DOI: 10.1016/j.neunet.2024.106974.Peer-Reviewed Original ResearchFeature spaceClassification performanceHeterogeneous transfer learningTensor dictionary learningHeterogeneous knowledge sharingTransfer learning frameworkReduce training costsDictionary learningKnowledge sharing strategyHeterogeneous transferGender classificationTransfer learningLearning frameworkConnectivity dataHeterogeneous dataHeterogeneous knowledgeBrain activity dataPriori knowledgeTraining costsSharing strategyProblem of insufficient sample sizeKnowledge sharingEEG dataExperimental resultsDictionaryA Cross-Feature Mutual Learning Framework to Integrate Functional Connectivity and Activity for Brain Disorder Classification
Zhao M, Xu R, Zhi D, Yu S, Calhoun V, Sui J. A Cross-Feature Mutual Learning Framework to Integrate Functional Connectivity and Activity for Brain Disorder Classification. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40038938, DOI: 10.1109/embc53108.2024.10781810.Peer-Reviewed Original ResearchConceptsLearning frameworkMutual learning frameworkEnd-to-endDeep learning approachMutual knowledge transferEnsemble decisionClassification performanceCross featuresJoint lossLearning approachNetwork connectivityKnowledge transferEncodingAdaptive integrationIndependent componentsCollaborative learningDynamic dependenceTC-specificRobust characteristicsLearningStudy of brain disordersDisorder classificationEmpirical resultsCross-modal modulationAccuracyCGDM-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 wayDataTaskLabel Noise-Robust Ensemble Deep Multimodal Framework For Neuroimaging Data
Rokham H, Falakshahi H, Calhoun V. Label Noise-Robust Ensemble Deep Multimodal Framework For Neuroimaging Data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039505, DOI: 10.1109/embc53108.2024.10782672.Peer-Reviewed Original ResearchConceptsLabel noiseEffects of label noiseBrain-based markersSelf-report assessmentsLabel noise problemFunctional MRI dataDeep convolutional frameworkDeep learning modelsK-fold cross-validation techniqueAssessment of diagnosisNosological categoriesCross-validation techniqueNeuroimaging dataMental illnessClassification performanceConvolutional frameworkDiagnostic categoriesDiagnostic classificationEnsemble methodsMultimodal frameworkLearning modelsSubsets of dataBagging approachK-foldNeuroimagingVoxelwise Intensity Projection for the Spatial Representation of Resting State Functional MRI Networks and Multimodal Deep Learning
Itkyal V, Abrol A, LaGrow T, Calhoun V. Voxelwise Intensity Projection for the Spatial Representation of Resting State Functional MRI Networks and Multimodal Deep Learning. 2024, 00: 1-4. DOI: 10.1109/isbi56570.2024.10635831.Peer-Reviewed Original ResearchMulti-channel convolutional neural networkConvolutional neural networkMultimodal deep learningSpatial information extractionAmplitude of low-frequency fluctuationSaliency visualizationInformation extractionClassification performanceDeep learningAlzheimer's Disease Neuroimaging Initiative datasetFusion resultsNeural networkAUC scoreAD classificationTemporal dependenciesFMRI networksTraditional metricsTest accuracyInitiative datasetNetworkLow-frequency fluctuationsMaximum 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 effectsDataset
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