2023
Outlier Robust Disease Classification via Stochastic Confidence Network
Lee K, Lee H, El Fakhri G, Sepulcre J, Liu X, Xing F, Hwang J, Woo J. Outlier Robust Disease Classification via Stochastic Confidence Network. Lecture Notes In Computer Science 2023, 14394: 80-90. DOI: 10.1007/978-3-031-47425-5_8.Peer-Reviewed Original ResearchDeep learningState-of-the-art modelsAccuracy of deep learningState-of-the-artMedical image dataMedical imaging modalitiesImage patchesIrrelevant patchesCategorical featuresPresence of outliersDL modelsConfidence networkConfidence predictionsClassifying outliersData samplesImage dataOutliersExperimental resultsDisease classificationImprove diagnostic performanceClassificationDiagnosing breast tumorsUltrasound imagingPerformanceImages
2020
A Physics-Informed Geometric Learning Model for Pathological Tau Spread in Alzheimer’s Disease
Song T, Chowdhury S, Yang F, Jacobs H, Sepulcre J, Wedeen V, Johnson K, Dutta J. A Physics-Informed Geometric Learning Model for Pathological Tau Spread in Alzheimer’s Disease. Lecture Notes In Computer Science 2020, 12267: 418-427. PMID: 33263115, PMCID: PMC7700821, DOI: 10.1007/978-3-030-59728-3_41.Peer-Reviewed Original ResearchGraph neural networksNeural networkPeak signal-to-noise ratioLearning modelsPhysics-based regularizationSignal-to-noise ratioLatent spaceData sizeDifferential equation solverDiffusion tensor imagingLearning patternsImage dataConnected graphStructural connectivity graphsPositron emission tomographyHarvard Aging Brain StudyNetworkAlzheimer's diseaseAging Brain StudyGraphEffective trainingCross-validation studyTau spreadingBrain regionsEquation solver
2015
A 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 Research