2025
Exploring the potential of generative artificial intelligence in medical image synthesis: opportunities, challenges, and future directions
Khosravi B, Purkayastha S, Erickson B, Trivedi H, Gichoya J. Exploring the potential of generative artificial intelligence in medical image synthesis: opportunities, challenges, and future directions. The Lancet Digital Health 2025, 100890. PMID: 40816978, DOI: 10.1016/j.landig.2025.100890.Peer-Reviewed Original ResearchGenerative artificial intelligenceArtificial intelligenceMedical imagesSynthetic datasetsSynthetic dataMedical image synthesisReal-world dataPrivacy preservationImage synthesisData copiesPhysics-InformedDisease datasetRobustness evaluation frameworkRadiology workflowPatient privacyIntelligenceDatasetPrivacyEvaluation frameworkGeneration paradigmImagesResearch resourcesStatistical modelApplicationsWorkflowClustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data
Li Z, Windels S, Malod-Dognin N, Weinberg S, Marazita M, Walsh S, Shriver M, Fardo D, Claes P, Pržulj N, Van Steen K. Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data. Bioinformatics 2025, 41: btaf122. PMID: 40119919, PMCID: PMC11978392, DOI: 10.1093/bioinformatics/btaf122.Peer-Reviewed Original ResearchConceptsNonnegative matrix tri-factorizationMulti-view clustering methodsClustering methodMulti-view clusteringLow-rank embeddingDrivers of clusteringMatrix tri-factorizationBiologically meaningful interpretationSingle-view approachImage dataAdjusted Rand IndexEmbedding vectorsEmbedding frameworkFacial annotationsOmics dataSynthetic datasetsTri-factorizationRelevant embeddingsRand indexClusters of individualsOmicsComprehensive clusteringEmbeddingCluster individualsExternal quality
2024
A machine learning framework to adjust for learning effects in medical device safety evaluation
Koola J, Ramesh K, Mao J, Ahn M, Davis S, Govindarajulu U, Perkins A, Westerman D, Ssemaganda H, Speroff T, Ohno-Machado L, Ramsay C, Sedrakyan A, Resnic F, Matheny M. A machine learning framework to adjust for learning effects in medical device safety evaluation. Journal Of The American Medical Informatics Association 2024, 32: 206-217. PMID: 39471493, PMCID: PMC11648715, DOI: 10.1093/jamia/ocae273.Peer-Reviewed Original ResearchMachine Learning FrameworkSynthetic datasetsLearning frameworkMachine learningCapacity of MLLearning effectFeature correlationDepartment of Veterans AffairsSynthetic dataData generationAbsence of learning effectsTraditional statistical methodsML methodsSuperior performanceDatasetSafety signal detectionSignal detectionDevice signalsVeterans AffairsTime-varying covariatesLearningMachinePhysician experienceLimitations of traditional statistical methodsMedical device post-market surveillanceCGDM-GAN: An Adversarial Network Approach with Self-supervised Learning for Site Effect Removal
Cui X, Zhi D, Yan W, Calhoun V, Zhuo C, Sui J. CGDM-GAN: An Adversarial Network Approach with Self-supervised Learning for Site Effect Removal. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039732, DOI: 10.1109/embc53108.2024.10782176.Peer-Reviewed Original ResearchConceptsSelf-supervised learningIntrinsic image propertiesGeneralization of modelsSynthetic datasetsClassification performanceGenerative modelDiscrepancy minimizationImage dataNetwork approachDatasetData harmonizationImaging propertiesLearningNeuroimaging classificationCycleGANData harmonization methodsAdversaryABCD datasetAcquisition protocolsPerformanceEffective wayDataTask
2023
Multimodal Subspace Independent Vector Analysis Better Captures Hidden Relationships in Multimodal Neuroimaging Data
Li X, Adali T, Silva R, Calhoun V. Multimodal Subspace Independent Vector Analysis Better Captures Hidden Relationships in Multimodal Neuroimaging Data. 2023, 00: 1-5. DOI: 10.1109/isbi53787.2023.10230605.Peer-Reviewed Original ResearchSubspace structureIndependent vector analysisSynthetic datasetsMultimodal neuroimaging datasetUnimodal analysisData modalitiesHidden relationshipsCanonical correlation analysisIncorrect onesNeuroimaging datasetsSubspaceLatent sourcesDatasetNeuroimaging modalitiesDataPhenotypic measurementsCorrelation analysisSimulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance
Davis S, Ssemaganda H, Koola J, Mao J, Westerman D, Speroff T, Govindarajulu U, Ramsay C, Sedrakyan A, Ohno-Machado L, Resnic F, Matheny M. Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance. BMC Medical Research Methodology 2023, 23: 89. PMID: 37041457, PMCID: PMC10088292, DOI: 10.1186/s12874-023-01913-9.Peer-Reviewed Original ResearchConceptsSynthetic datasetsData characteristicsFeature distributionGround truthMIMIC-III dataReal-world dataData generation processComplex simulation studiesData relationshipsUser definitionSmall datasetsSimulation requirementsCorrelated featuresWorld dataCustomizable optionsReal-world complexitySynthetic patientsNew algorithmDatasetGeneration processLearningAlgorithmData simulation techniquesLearning effectGeneralizable frameworkIs Attention Interpretation? A Quantitative Assessment on Sets
Haab J, Deutschmann N, Martínez M. Is Attention Interpretation? A Quantitative Assessment on Sets. Communications In Computer And Information Science 2023, 1752: 303-321. DOI: 10.1007/978-3-031-23618-1_21.Peer-Reviewed Original ResearchBinary classification problemInterpretation of attentionClassification problemAttention mechanismSynthetic datasetsUnordered collectionClassification performanceSilent failuresMachine learningGlobal labelsData modalitiesIndividual instancesAttention distributionAttention scoresAttention patternsData pointsSub-componentsInstancesDataset
2019
Protecting patient privacy in survival analyses
Bonomi L, Jiang X, Ohno-Machado L. Protecting patient privacy in survival analyses. Journal Of The American Medical Informatics Association 2019, 27: 366-375. PMID: 31750926, PMCID: PMC7025359, DOI: 10.1093/jamia/ocz195.Peer-Reviewed Original ResearchConceptsPrivacy protectionPrivacy risksHealthcare applicationsPatient privacyPrivacy protection methodProvable privacy protectionStrong privacy protectionPerson of interestKnowledgeable adversaryDifferential privacySynthetic datasetsFormal modelEpidemiology datasetPrivacyNonparametric survival modelFuture research directionsAdversaryResearch directionsDatasetBiomedical research applicationsFrameworkFrequent sharingResearch applicationsApplicationsSharingNetwork-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer
Oskooei A, Manica M, Mathis R, Martínez M. Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer. Scientific Reports 2019, 9: 15918. PMID: 31685861, PMCID: PMC6828742, DOI: 10.1038/s41598-019-52093-w.Peer-Reviewed Original ResearchConceptsMembrane receptor pathwayDrug sensitivity predictionProtein-protein interaction networkDrug sensitivityGenomics of Drug SensitivityDrug targetsGene expression dataIGFR signaling pathwaysAssignment of high weightsBiomarker identificationExpression dataInteraction networkSensitivity predictionSignaling pathwaySignaling pathway inhibitorsReceptor pathwayTree ensemblesPathway inhibitorPathwayGenomeGenesGDSCNeighborhoods of influenceIdentificationSynthetic datasets
2014
Quantitative Arbor Analytics: Unsupervised Harmonic Co-Clustering of Populations of Brain Cell Arbors Based on L-Measure
Lu Y, Carin L, Coifman R, Shain W, Roysam B. Quantitative Arbor Analytics: Unsupervised Harmonic Co-Clustering of Populations of Brain Cell Arbors Based on L-Measure. Neuroinformatics 2014, 13: 47-63. PMID: 25086878, DOI: 10.1007/s12021-014-9237-2.Peer-Reviewed Original ResearchConceptsCo-clustering methodAnalytics systemSynthetic datasetsThree-dimensional visualizationAnalysis ToolkitHeterogeneous ensembleDistance measureAlgorithmMultivariate data pointsData smoothingData pointsWavelet basisData matrixHarmonic analysis theoryL-measureNeuroMorpho databaseDatasetAnalysis theoryToolkitVisualizationEnsembleRobustnessDatabaseSuperiorityMethod
2010
Variable Down-Selection for Brain-Computer Interfaces
Dias N, Kamrunnahar M, Mendes P, Schiff S, Correia J. Variable Down-Selection for Brain-Computer Interfaces. Communications In Computer And Information Science 2010, 52: 158-172. DOI: 10.1007/978-3-642-11721-3_12.Peer-Reviewed Original ResearchBrain-computer interfaceLarge feature spaceBetter classification performanceLarge dimensionality reductionSynthetic datasetsClassification performanceFeature spaceClassification errorDimensionality reductionBCI datasetsImagery tasksElectrode channelsMovement imagery tasksKey problemVariable subsetHigh performanceAnalysis classifierBCI experimentsAlgorithmDatasetPrincipal component analysisTaskTime limitationsClassifier
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