A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data
Rokham H, Falakshahi H, Calhoun V. A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38082903, DOI: 10.1109/embc40787.2023.10339949.Peer-Reviewed Original ResearchConceptsStructural MRI dataResting-state functional MRI dataFunctional MRI dataFunctional magnetic resonance imaging dataMRI dataMagnetic resonance imaging dataSchizophrenia patientsFunctional connectivity featuresBrain imaging modalitiesMental disordersNeuroimaging dataNeuroimaging techniquesBorderline subjectsHealthy control groupSchizophrenia datasetSchizophreniaConnectivity featuresBrainPsychosisMoodNosologyControl groupDisordersLabel noiseSubjectsMulti-Modal Deep Learning on Imaging Genetics for Schizophrenia Classification
Kanyal A, Kandula S, Calhoun V, Ye D. Multi-Modal Deep Learning on Imaging Genetics for Schizophrenia Classification. 2023, 00: 1-5. DOI: 10.1109/icasspw59220.2023.10193352.Peer-Reviewed Original ResearchSingle nucleotide polymorphismsSZ patientsFunctional network connectivityFunctional MRIStructural MRIFunctional brain connectivityGenetic markersChronic mental conditionsNucleotide polymorphismsBrain connectivity featuresDecreased hippocampalGenetic featuresSZ subjectsMorphological changesThalamic volumeBrain connectivitySZ diagnosisGenetic illnessMental conditionLayer-wise relevance propagationMorphological featuresBrainConnectivity featuresSZ