2024
GPR-SCSANet: Unequal-Length Time Series Normalization with Split-Channel Residual Convolution and Self-Attention for Brain Age Prediction
Sun F, Liang C, Adali T, Zhang D, Jiang R, Calhoun V, Qi S. GPR-SCSANet: Unequal-Length Time Series Normalization with Split-Channel Residual Convolution and Self-Attention for Brain Age Prediction. 2024, 00: 5097-5103. DOI: 10.1109/bibm62325.2024.10822453.Peer-Reviewed Original ResearchSelf-attention mechanismResidual convolutionGaussian process regressionFunctional magnetic resonance imagingReal-world scenariosAge prediction taskSelf-attentionPrediction taskBrain age estimationAge predictionInherent informationBrain age predictionFMRI time coursesLength of time seriesProcess regressionVariables conflictBrain functional alterationsConvolutionPrediction accuracyUnequal-lengthTraditional methodsMotion artifactsDownstream applicationsTime series normalizationPrediction model
2023
An Adaptive Semi-Supervised Deep Clustering and Its Application to Identifying Biotypes of Psychiatric Disorders
Du Y, Wu F, Niu J, Calhoun V. An Adaptive Semi-Supervised Deep Clustering and Its Application to Identifying Biotypes of Psychiatric Disorders. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230805.Peer-Reviewed Original ResearchFashion-MNIST dataDeep clustering methodsFunctional magnetic resonance imagingMNIST dataAutism spectrum disorderClustering methodPsychiatric disordersSemi-supervised clusteringPsychiatric disorder symptomsUnlabeled samplesClustering performanceDeep clusteringLabeled samplesDeep learningClustering techniqueDisorder symptomsSpectrum disorderNeuroimaging dataUseful informationSchizophreniaTraditional methodsMagnetic resonance imagingDisordersResonance imagingHigh confidence level
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