2024
Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography
Tran A, Desser D, Zeevi T, Karam G, Zietz J, Dell’Orco A, Chen M, Malhotra A, Qureshi A, Murthy S, Majidi S, Falcone G, Sheth K, Nawabi J, Payabvash S. Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography. Applied Sciences 2024, 15: 111. PMID: 40046237, PMCID: PMC11882137, DOI: 10.3390/app15010111.Peer-Reviewed Original ResearchIntracerebral hemorrhageHematoma expansionFollow-up CT scansFollow-up head computed tomographyPredictors of poor outcomeDeep learning classification modelFollow-up scansHead computed tomographyFalse-negative resultsHematoma segmentationAutomated segmentationMulticentre cohortCT scanValidation cohortPoor outcomeComputed tomographyFollow-upClassification modelOptimizational methodHematomaAnnotation
2023
Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG
Yan W, Yu L, Liu D, Sui J, Calhoun V, Lin Z. Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG. Frontiers In Psychiatry 2023, 14: 1202049. PMID: 37441141, PMCID: PMC10333510, DOI: 10.3389/fpsyt.2023.1202049.Peer-Reviewed Original ResearchConvolutional recurrent neural networkRecurrent neural networkResting-state EEGNeural networkPsychiatric disordersDeep learning classification modelLow-dimensional subspaceTwo-class classificationDesigning individualized treatmentLearning classification modelsEEG backgroundClassification modelHealthy controlsDepressive disorderSpatiotemporal informationClinical observationsDisease severityAccurate classificationIndividualized treatmentBiomarkersDisorder classificationDisorder identificationDisordersClassificationNeuroimaging biomarkers
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