2024
Exploring Backdoor Attacks in Off-the-Shelf Unsupervised Domain Adaptation for Securing Cardiac MRI-Based Diagnosis
Liu X, Xing F, Gaggin H, Kuo C, El Fakhri G, Woo J. Exploring Backdoor Attacks in Off-the-Shelf Unsupervised Domain Adaptation for Securing Cardiac MRI-Based Diagnosis. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2024, 00: 1-5. PMID: 39421190, PMCID: PMC11483644, DOI: 10.1109/isbi56570.2024.10635403.Peer-Reviewed Original ResearchUnsupervised domain adaptationTarget domain modelBackdoor attacksDomain adaptationTraining dataLabeled source domain dataSusceptible to backdoor attacksAccurate pseudo labelsDomain modelSource domain dataPatient data privacyTarget training dataOff-the-shelfPseudo-labelsData privacySource domainMulti-vendorRandom initializationTraining phaseDomain dataDiagnosis modelTarget modelMulti-diseaseAttacksAuxiliary model
2023
Attentive continuous generative self-training for unsupervised domain adaptive medical image translation
Liu X, Prince J, Xing F, Zhuo J, Reese T, Stone M, El Fakhri G, Woo J. Attentive continuous generative self-training for unsupervised domain adaptive medical image translation. Medical Image Analysis 2023, 88: 102851. PMID: 37329854, PMCID: PMC10527936, DOI: 10.1016/j.media.2023.102851.Peer-Reviewed Original ResearchConceptsUnsupervised domain adaptationImage translationProblem of domain shiftSelf-trainingImage modality translationLabeled source domainTarget domain dataSelf-attention schemeAlternating optimization schemeHeterogeneous target domainContinuous value predictionPseudo-labelsDomain adaptationUDA methodsDomain shiftSoftmax probabilitiesSource domainTarget domainVariational BayesBackground regionsTranslation tasksTraining processDomain dataGeneration taskOptimization scheme
2022
Memory consistent unsupervised off-the-shelf model adaptation for source-relaxed medical image segmentation
Liu X, Xing F, El Fakhri G, Woo J. Memory consistent unsupervised off-the-shelf model adaptation for source-relaxed medical image segmentation. Medical Image Analysis 2022, 83: 102641. PMID: 36265264, PMCID: PMC10016738, DOI: 10.1016/j.media.2022.102641.Peer-Reviewed Original ResearchConceptsUnsupervised domain adaptationUnsupervised domain adaptation methodsSource domain dataBN statisticsTarget domainLabeled source domain dataDomain dataLabeled source domainSelf-training strategyPatient data privacyHeterogeneous target domainBrain tumor segmentationPseudo-labelsDomain adaptationUnsupervised adaptationData privacySegmentation taskSource domainImage segmentationVital protocolAdaptation frameworkDecay strategyBoost performanceModel adaptationTumor segmentationACT: Semi-supervised Domain-Adaptive Medical Image Segmentation with Asymmetric Co-training
Liu X, Xing F, Shusharina N, Lim R, Jay Kuo C, El Fakhri G, Woo J. ACT: Semi-supervised Domain-Adaptive Medical Image Segmentation with Asymmetric Co-training. Lecture Notes In Computer Science 2022, 13435: 66-76. PMID: 36780245, PMCID: PMC9911133, DOI: 10.1007/978-3-031-16443-9_7.Peer-Reviewed Original ResearchSemi-supervised domain adaptationUnsupervised domain adaptationSemi-supervised learningMedical image segmentationDomain adaptationDomain shiftLabel supervisionTarget domainImage segmentationDomain dataLeverage different knowledgePseudo-label noiseSignificant domain shiftSupervised joint trainingLabeled source domainUnlabeled target dataUnlabeled target domainLabeled target samplesTarget domain dataSource domain dataState-of-the-artMRI segmentation taskSubstantial performance gainsPseudo-labelsLabel noiseUnsupervised Black-Box Model Domain Adaptation for Brain Tumor Segmentation
Liu X, Yoo C, Xing F, Kuo C, Fakhri G, Kang J, Woo J. Unsupervised Black-Box Model Domain Adaptation for Brain Tumor Segmentation. Frontiers In Neuroscience 2022, 16: 837646. PMID: 35720708, PMCID: PMC9201342, DOI: 10.3389/fnins.2022.837646.Peer-Reviewed Original ResearchUnsupervised domain adaptationDomain adaptationSource domainTarget domainLabeled source domain to unlabeled target domainTransfer of domain knowledgeTarget-specific representationsUnlabeled target domainTarget domain dataKnowledge distillation schemeDeep learning backbonesEntropy minimizationTrained model parametersDifficulty of labelingDomain knowledgeSensitive informationPrivacy concernsPerformance gainsNetwork parametersSegmentation modelDomain dataSource dataCross-center collaborationDistillation schemePotential leaksUnsupervised domain adaptation for segmentation with black-box source model
Liu X, Yoo C, Xing F, Kuo C, El Fakhri G, Kang J, Woo J. Unsupervised domain adaptation for segmentation with black-box source model. Proceedings Of SPIE--the International Society For Optical Engineering 2022, 12032: 1203210-1203210-6. PMID: 35983176, PMCID: PMC9385170, DOI: 10.1117/12.2607895.Peer-Reviewed Original ResearchUnsupervised domain adaptationSource domainDomain adaptationTarget-specific representationsLabeled source domainUnlabeled target domainTarget domain dataWell-labeled dataKnowledge distillation schemeTrained model parametersModel adaptation approachOriginal source dataDifficulty of labelingTarget domainSegmentation modelDomain dataTransfer knowledgeEntropy minimizationAdaptive approachSource dataConventional solutionsPractical solutionDistillation schemePrivacyLarge-scaleDeep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives
Liu X, Yoo C, Xing F, Oh H, Fakhri G, Kang J, Woo J. Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives. APSIPA Transactions On Signal And Information Processing 2022, 11: e25. DOI: 10.1561/116.00000192.Peer-Reviewed Original ResearchUnsupervised domain adaptationTarget domainLabeled source domain dataOut-of-distribution detectionUnlabeled target domain dataOut-of-distribution dataDomain dataTarget domain dataOut-of-distributionSource domain dataDeep neural networksNatural image processingMedical image analysisNatural language processingReal-world problemsDomain adaptationLabeled datasetSource domainDomain generalizationDeep learningNeural networkLanguage processingImpressive performanceTime series data analysisPerformance drop
2021
Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation
Liu X, Xing F, Yang C, El Fakhri G, Woo J. Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation. Lecture Notes In Computer Science 2021, 12902: 549-559. PMID: 34734216, PMCID: PMC8562716, DOI: 10.1007/978-3-030-87196-3_51.Peer-Reviewed Original ResearchUnsupervised domain adaptationSegmentation taskSource domainTarget domainUnsupervised domain adaptation methodsLabeled source domainSource domain dataUnsupervised learning methodDomain adaptationUDA methodsPrivacy issuesLearning methodsAdaptation frameworkDomain dataData storageTransfer knowledgeBatch statisticsSource dataOptimization objectivesAdaptation stageTaskFrameworkPrivacyDomainBraTS