A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data
Bi Y, Abrol A, Fu Z, Calhoun V. A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data. Human Brain Mapping 2024, 45: e26783. PMID: 39600159, PMCID: PMC11599617, DOI: 10.1002/hbm.26783.Peer-Reviewed Original ResearchConceptsCross-attention mechanismVision transformerDeep learning modelsBrain disordersCharacteristics of schizophreniaDiagnosis of schizophreniaStructural neuroimaging dataNetwork connectivity matrixData fusion approachAttention mapsMultimodal baselinesFunctional network connectivityFuse informationDeep learningICA algorithmFusion approachGrey matter mapsAI algorithmsFunctional network connectivity matricesLeverage multiple sources of informationGray matter imagesLearning modelsMultiple sources of informationBrain imaging modalitiesNetwork connectivityNeuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade
Du Y, Niu J, Xing Y, Li B, Calhoun V. Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade. Schizophrenia Bulletin 2024, 51: 325-342. PMID: 38982882, PMCID: PMC11908864, DOI: 10.1093/schbul/sbae110.Peer-Reviewed Original ResearchArtificial intelligenceSemi-supervised learning methodArtificial intelligence techniquesAccurate diagnosis of SZMultimodal fusionAccurate diagnosis of schizophreniaIntelligence techniquesAI methodsLearning methodsDiagnosis of SZMental disordersSelection methodUnsupervised clusteringMagnetic resonance imagingBiomarker extractionDiagnosis of schizophreniaIdentifying Reproducibly Important EEG Markers of Schizophrenia with an Explainable Multi-Model Deep Learning Approach
Sancho M, Ellis C, Miller R, Calhoun V. Identifying Reproducibly Important EEG Markers of Schizophrenia with an Explainable Multi-Model Deep Learning Approach. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039893, DOI: 10.1109/embc53108.2024.10781959.Peer-Reviewed Original ResearchConceptsDeep learning approachLearning-based studiesMachine learning methodsMachine learning modelsMachine learning-based studiesExplainability approachesCross-validation foldsLearning methodsLearning approachLearning modelsDevelopment of robust approachesMachineDiagnosis of schizophreniaDiverse symptom presentationsPower dataBiomarkers of SZRobust approachFrequency bandLeft hemisphereSpectral power data
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