2024
CGDM-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 wayDataTaskThree-Dimensional Reconstruction Pre-Training as a Prior to Improve Robustness to Adversarial Attacks and Spurious Correlation
Yamada Y, Zhang F, Kluger Y, Yildirim I. Three-Dimensional Reconstruction Pre-Training as a Prior to Improve Robustness to Adversarial Attacks and Spurious Correlation. Entropy 2024, 26: 258. PMID: 38539769, PMCID: PMC10968904, DOI: 10.3390/e26030258.Peer-Reviewed Original ResearchAdversarial trainingPre-trainingAdversarial attacksAdversarial robustnessRobustness of image classifiersModel of human visionComputational model of human visionAdversarial examplesImage classifierWeight initializationDataset settingData augmentationBackground textureSpurious correlationsHuman visionModels of visionImprove robustnessDatasetRobustnessImage formationAttacksComputational modelTrainingShapeNetAdversary
2021
Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate
Liu X, Guo Z, Li S, Xing F, You J, Kuo C, Fakhri G, Woo J. Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate. 2021, 00: 10347-10356. DOI: 10.1109/iccv48922.2021.01020.Peer-Reviewed Original ResearchUnsupervised domain adaptationDomain adaptationLabel shiftUnsupervised domain adaptation methodsAdversarial unsupervised domain adaptationAlternating optimization schemeUDA methodsTarget domainTraining stageOptimization schemeTesting stageExperimental resultsDistribution w.AdversaryP(x|yP(y|xDomainSchemeClassificationMethodInferenceAdaptation
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 applicationsApplicationsSharing
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