Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images
Shaik N, Cherukuri T, Calhoun V, Ye D. Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images. 2024, 00: 1-5. DOI: 10.1109/isbi56570.2024.10635528.Peer-Reviewed Original ResearchCognitive abilitiesStructural MRIAttention mechanismSchizophrenia classificationChronic mental disordersIndividual cognitive abilitiesTransfer learning paradigmDeep learning methodologyMental disordersSchizophreniaFeature mapsFeature representationConvolutional blocksAttention networkBrain MR imagesLearning paradigmSocial interactionClassification of individualsStructural brain MR imagesGray matterLearning methodologyExcitable networksClinical datasetsBrainManual observationInterpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks
Qu G, Orlichenko A, Wang J, Zhang G, Xiao L, Zhang K, Wilson T, Stephen J, Calhoun V, Wang Y. Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks. IEEE Transactions On Medical Imaging 2024, 43: 1568-1578. PMID: 38109241, PMCID: PMC11090410, DOI: 10.1109/tmi.2023.3343365.Peer-Reviewed Original ResearchConceptsGraph transformation frameworkBrain imaging datasetsFunctional brain networksPhiladelphia Neurodevelopmental CohortConvolutional deep learningFeature embeddingPropagation weightsGraph embeddingHuman Connectome ProjectAttention mechanismImage datasetsDeep learningGraph transformationFunctional connectivityAnalyze functional brain networksTransformation frameworkDiffusion strategyBrain networksPositional encodingSpatial knowledgePrediction accuracyIndividual cognitive abilitiesEmbeddingNetworkGraph