2021
BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis
Li X, Zhou Y, Dvornek N, Zhang M, Gao S, Zhuang J, Scheinost D, Staib LH, Ventola P, Duncan JS. BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis. Medical Image Analysis 2021, 74: 102233. PMID: 34655865, PMCID: PMC9916535, DOI: 10.1016/j.media.2021.102233.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagesGraph neural network frameworkMedical image analysisGraph neural networkGraph convolutional layersNeural network frameworkDifferent evaluation metricsSpecific task statesIndependent fMRI datasetsPooling layerConvolutional layersConsistency lossNetwork frameworkNeural networkFMRI datasetsImage analysis methodEvaluation metricsDetection resultsBrain graphsSubjects releaseROI selectionImage analysisCognitive stimuliTask statesFMRI analysis
2018
Learning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets
Dvornek NC, Yang D, Ventola P, Duncan JS. Learning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets. Lecture Notes In Computer Science 2018, 11072: 329-337. PMID: 30873514, PMCID: PMC6411297, DOI: 10.1007/978-3-030-00931-1_38.Peer-Reviewed Original ResearchConceptsRecurrent neural networkNeural networkTask fMRI datasetsMedical image analysis problemsSuch deep networksImage analysis problemsTask fMRI scanTypical control subjectsDeep networkDeep learningTraining lossSmall datasetsLarge datasetsNumber of approachesAutism spectrum disorderAnalysis problemDatasetNetworkTraining runsImage analysisGeneralizable modelNon-imaging variablesSpectrum disorderFMRI analysisModel performancePrediction of Severity and Treatment Outcome for ASD from fMRI
Zhuang J, Dvornek NC, Li X, Ventola P, Duncan JS. Prediction of Severity and Treatment Outcome for ASD from fMRI. Lecture Notes In Computer Science 2018, 11121: 9-17. PMID: 32984867, PMCID: PMC7513883, DOI: 10.1007/978-3-030-00320-3_2.Peer-Reviewed Original ResearchFeature selectionMedical image analysis problemsMedical image analysisLimited training examplesImage analysis problemsDimension of dataFeature selection methodHigh-dimensional regression problemsTraining examplesTwo-level approachAccurate predictive modelsRegression problemsHigh dimensionalityLarge databaseRandom forest modelSelection methodNon-linear caseAnalysis problemNumber of voxelsImage analysisForest modelState fMRI datasetsMeans of voxelAccurate modelFMRI datasets
2016
Towards patient-specific modeling of mitral valve repair: 3D transesophageal echocardiography-derived parameter estimation
Zhang F, Kanik J, Mansi T, Voigt I, Sharma P, Ionasec RI, Subrahmanyan L, Lin BA, Sugeng L, Yuh D, Comaniciu D, Duncan J. Towards patient-specific modeling of mitral valve repair: 3D transesophageal echocardiography-derived parameter estimation. Medical Image Analysis 2016, 35: 599-609. PMID: 27718462, DOI: 10.1016/j.media.2016.09.006.Peer-Reviewed Original ResearchConceptsMitral valve modelingTemporal informationPatient-specific modelingImage acquisitionEuclidean distanceValve modelingComputational frameworkExtended Kalman filterImage analysisModeling frameworkKalman filterFrameworkAverage errorMitral valve geometryTEE imagesInformationMachineParameter estimationClosed mitral valveLeaflet material propertiesSubjective predictionModelingImagesRepresentationOptimization