2019
Graph Convolutional Neural Networks For Alzheimer’s Disease Classification
Song T, Roy Chowdhury S, Yang F, Jacobs H, El Fakhri G, Li Q, Johnson K, Dutta J. Graph Convolutional Neural Networks For Alzheimer’s Disease Classification. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2019, 00: 414-417. PMID: 31327984, PMCID: PMC6641559, DOI: 10.1109/isbi.2019.8759531.Peer-Reviewed Original ResearchGraph convolutional neural networkConvolutional neural networkNeural networkCapabilities of convolutional neural networksGraph-structured dataNon-Euclidean domainsClassification capability of convolutional neural networksVector machine classifierGraph-based toolsData representationAudio signalsClassification capabilityMachine classifierClassifierPerformance gapImage dataNetworkConnected graphStructural connectivity graphsDisease classificationClassificationBrain connectivity studiesEuclidean domainsComplex systemsGraphEMnet: an unrolled deep neural network for PET image reconstruction
Gong K, Wu D, Kim K, Yang J, Fakhri G, Seo Y, Li Q. EMnet: an unrolled deep neural network for PET image reconstruction. Progress In Biomedical Optics And Imaging 2019, 10948: 1094853-1094853-6. DOI: 10.1117/12.2513096.Peer-Reviewed Original ResearchDeep neural networksPET image reconstructionNeural networkExpectation maximizationImage reconstructionImage denoising applicationNeural network frameworkNeural network denoisersDenoising applicationsDenoising methodNetwork denoisingNetwork trainingNetwork frameworkWhole graphUpdate stepData consistencyIll-posedNetworkInverse problemEMNETDenoisingSimulated dataFrameworkAlgorithmGraph
2015
Matched signal detection on graphs: Theory and application to brain imaging data classification
Hu C, Sepulcre J, Johnson K, Fakhri G, Lu Y, Li Q. Matched signal detection on graphs: Theory and application to brain imaging data classification. NeuroImage 2015, 125: 587-600. PMID: 26481679, DOI: 10.1016/j.neuroimage.2015.10.026.Peer-Reviewed Original ResearchConceptsImage data classificationWeighted energy detectorGraph-signalGraph Laplacian eigenvaluesEnergy detectorManifold structureProblem of Alzheimer's diseaseData classificationGraph LaplacianSubspace detectorWeighted graphMSD approachSignal processingSignal detectionIntrinsic structureLaplacian eigenvaluesSubspaceTest statisticsGraphRandom signalsData setsLowest eigenvalueGaussian distributionTraditional methodsEigenvaluesA Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer’s Disease
Hu C, Cheng L, Sepulcre J, Johnson K, Fakhri G, Lu Y, Li Q. A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer’s Disease. PLOS ONE 2015, 10: e0128136. PMID: 26024224, PMCID: PMC4449104, DOI: 10.1371/journal.pone.0128136.Peer-Reviewed Original ResearchConceptsNetwork featuresAlzheimer's diseaseConsistent with known pathologyUnknown graphConnection weightsReconstruction networkCortical hubsDegree statisticsData modelSmooth signalsFeatures of brain pathologyOptimization frameworkAmyloid-bPartial correlation estimationImage dataNetworkGraphGlobal connectivity measuresPositron emission tomographyConnectivity measuresNeurodegenerative diseasesConnectivity patternsSample correlationClinical ADSimulated data
2013
A GRAPH THEORETICAL REGRESSION MODEL FOR BRAIN CONNECTIVITY LEARNING OF ALZHEIMER'S DISEASE
Hu C, Cheng L, Sepulcre J, Fakhri G, Lu Y, Li Q. A GRAPH THEORETICAL REGRESSION MODEL FOR BRAIN CONNECTIVITY LEARNING OF ALZHEIMER'S DISEASE. 2013, 616-619. DOI: 10.1109/isbi.2013.6556550.Peer-Reviewed Original ResearchMatched Signal Detection on Graphs: Theory and Application to Brain Network Classification
Hu C, Cheng L, Sepulcre J, El Fakhri G, Lu Y, Li Q. Matched Signal Detection on Graphs: Theory and Application to Brain Network Classification. Lecture Notes In Computer Science 2013, 23: 1-12. PMID: 24683953, DOI: 10.1007/978-3-642-38868-2_1.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAlzheimer DiseaseAniline CompoundsBenzothiazolesBrainBrain MappingConnectomeHumansImage EnhancementImage Interpretation, Computer-AssistedNerve NetNeural PathwaysPattern Recognition, AutomatedPositron-Emission TomographyReproducibility of ResultsSensitivity and SpecificityThiazolesTissue DistributionConceptsBrain network classificationNetwork classification problemWeighted energy detectorPrinciple component analysisSub-manifold structureTraditional principle component analysisSubspace detectionTraining dataEnergy detectorGraph structureProblem of Alzheimer's diseaseGraph LaplacianNetwork classificationNoise varianceLevel of smoothnessWeighted graphSignal detectionIntrinsic structureSignal modelGraphSubspaceIsing modelNoiseSignal variationsComponent analysis