2025
Label Space-Induced Pseudo Label Refinement for Multi-Source Black-Box Domain Adaptation
Yoo C, Liu X, Xing F, Woo J, Kang J. Label Space-Induced Pseudo Label Refinement for Multi-Source Black-Box Domain Adaptation. IEEE Transactions On Image Processing 2025, 34: 3181-3193. PMID: 40397626, DOI: 10.1109/tip.2025.3570220.Peer-Reviewed Original ResearchUnsupervised domain adaptationApplication programming interfacePseudo-labelsDomain adaptationLabel refinementConventional unsupervised domain adaptationState-of-the-art approachesTarget modelInitial pseudo labelsState-of-the-artMulti-source settingBenchmark datasetsNoisy samplesSource domainTarget domainCompetitive performanceProgramming interfaceTraining frameworkSelf-trainingRefinement phaseSource dataMulti-sourceSource model parametersExperimental resultsRelationship explorationOrdinal Unsupervised Domain Adaptation With Recursively Conditional Gaussian Imposed Variational Disentanglement
Liu X, Li S, Ge Y, Ye P, You J, Lu J. Ordinal Unsupervised Domain Adaptation With Recursively Conditional Gaussian Imposed Variational Disentanglement. IEEE Transactions On Pattern Analysis And Machine Intelligence 2025, 47: 3219-3232. PMID: 35704544, DOI: 10.1109/tpami.2022.3183115.Peer-Reviewed Original ResearchUnsupervised domain adaptationDomain adaptationCross-domain imagesFacial age estimationLatent spaceScalability issuesContent spaceDiscrete labelsLatent vectorsOrdinal classificationContent vectorsSelf-trainingMedical diagnosisConstraint modelPartially ordered setSource/targetJoint distributionVectorLabelingTaskDataAge estimationClassificationSpaceRecursion
2024
Style mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation
Cai Z, Xin J, You C, Shi P, Dong S, Dvornek N, Zheng N, Duncan J. Style mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation. Medical Image Analysis 2024, 101: 103440. PMID: 39764933, DOI: 10.1016/j.media.2024.103440.Peer-Reviewed Original ResearchConceptsUnsupervised domain adaptationMedical image segmentationDomain-invariant representationsImage segmentationDomain adaptationDisentanglement learningImage translationUnsupervised domain adaptation approachState-of-the-art methodsDomain shift problemDomain-invariant learningState-of-the-artPublic cardiac datasetsDiverse constraintsAdversarial learningConsistency regularizationContrastive learningFeature spaceSemantic consistencyComprehensive experimentsDomain generalizationData diversityShift problemMedical segmentationCardiac datasetsPrior knowledge-guided vision-transformer-based unsupervised domain adaptation for intubation prediction in lung disease at one week
Yang J, Henao J, Dvornek N, He J, Bower D, Depotter A, Bajercius H, de Mortanges A, You C, Gange C, Ledda R, Silva M, Dela Cruz C, Hautz W, Bonel H, Reyes M, Staib L, Poellinger A, Duncan J. Prior knowledge-guided vision-transformer-based unsupervised domain adaptation for intubation prediction in lung disease at one week. Computerized Medical Imaging And Graphics 2024, 118: 102442. PMID: 39515190, DOI: 10.1016/j.compmedimag.2024.102442.Peer-Reviewed Original ResearchUnsupervised domain adaptationSpatial prior informationDomain adaptationLabeled dataData-driven approachUnsupervised domain adaptation modelMedical image analysis tasksImage analysis tasksTransformer-based modelsMedical image analysisPrior informationOutcome prediction tasksAdversarial trainingDistribution alignmentDomain shiftAttention headsClass tokenPoor generalizationAnalysis tasksTarget domainPrediction taskData distributionKnowledge-guidedLocal weightsMedical imagesExploring 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 modelSubtype-Aware Dynamic Unsupervised Domain Adaptation
Liu X, Xing F, You J, Lu J, Kuo C, Fakhri G, Woo J. Subtype-Aware Dynamic Unsupervised Domain Adaptation. IEEE Transactions On Neural Networks And Learning Systems 2024, 35: 2820-2834. PMID: 35895653, DOI: 10.1109/tnnls.2022.3192315.Peer-Reviewed Original ResearchTarget domainSource domain to target domainUnsupervised domain adaptationWithin-class compactnessHeart disease dataPseudo-labelsDomain adaptationClass centersLatent spaceCluster centroidsConditional alignmentLabel shiftTransfer knowledgeQueueing frameworkLocal proximityAlternative processing schemesSubtype labelsExperimental resultsProcessing schemeSubtype structureDomainNetVisDADisease dataDomainLabeling
2023
Unsupervised Domain Adaptation by Cross-Prototype Contrastive Learning for Medical Image Segmentation
Cai Z, Xin J, Dong S, You C, Shi P, Zeng T, Zhang J, Onofrey J, Zheng N, Duncan J. Unsupervised Domain Adaptation by Cross-Prototype Contrastive Learning for Medical Image Segmentation. 2023, 00: 819-824. DOI: 10.1109/bibm58861.2023.10386055.Peer-Reviewed Original ResearchUnsupervised domain adaptationDistribution alignmentDomain adaptationContrastive learningUnsupervised domain adaptation methodsMedical image segmentation tasksDomain distribution alignmentGlobal distribution alignmentContrastive learning methodDomain adaptation performanceIntra-class distancePixel-level featuresImage segmentation tasksInter-class distancePublic cardiac datasetsCategory centroidDiscrimination of classesClass prototypesSegmentation taskSource domainTarget domainCardiac datasetsLearning methodsGlobal prototypesCentroid alignmentSource-free domain adaptive segmentation with class-balanced complementary self-training
Huang Y, Xie W, Li M, Xiao E, You J, Liu X. Source-free domain adaptive segmentation with class-balanced complementary self-training. Artificial Intelligence In Medicine 2023, 146: 102694. PMID: 38042612, DOI: 10.1016/j.artmed.2023.102694.Peer-Reviewed Original ResearchUnsupervised domain adaptationPseudo-label noiseSelf-trainingSegmentation taskSource domainUnsupervised domain adaptation methodsBrain tumor segmentation taskDomain adaptive segmentationLabeled source domainPatient data privacyTumor segmentation taskDomain adaptationData privacyLabeled dataTarget domainSelection schemeAdaptive segmentationSource dataExperimental resultsMinor categoriesTaskNoiseIntellectual propertyPrivacySegmentorSelf-Supervised Domain Adaptive Segmentation of Breast Cancer via Test-Time Fine-Tuning
Lee K, Lee H, El Fakhri G, Woo J, Hwang J. Self-Supervised Domain Adaptive Segmentation of Breast Cancer via Test-Time Fine-Tuning. Lecture Notes In Computer Science 2023, 14220: 539-550. DOI: 10.1007/978-3-031-43907-0_52.Peer-Reviewed Original ResearchUnsupervised domain adaptationTarget domainState-of-the-art performanceUnsupervised domain adaptation modelWell-trained deep learning modelDomain adaptation tasksDomain adaptive segmentationState-of-the-artAdaptive feature extractionFine-tuning phaseFeatures of datasetsLarge-scale datasetsDeep learning modelsDomain adaptationUnlabeled dataLabeled dataSegmentation taskNetwork architectureSource domainFeature extractionLatent featuresModel deploymentNetwork parametersBreast cancer datasetAdaptive segmentationAttentive 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 noiseConstraining pseudo‐label in self‐training unsupervised domain adaptation with energy‐based model
Kong L, Hu B, Liu X, Lu J, You J, Liu X. Constraining pseudo‐label in self‐training unsupervised domain adaptation with energy‐based model. International Journal Of Intelligent Systems 2022, 37: 8092-8112. DOI: 10.1002/int.22930.Peer-Reviewed Original ResearchUnsupervised domain adaptationPseudo-labelsDomain adaptationEnergy-based modelTarget domainUnlabeled target samplesLabeled source domainUnlabeled target domainPlug-and-play fashionSemantic segmentationImage classificationSource domainDeep learningSelf-trainingExpectation MinimizationConvergence propertiesMinimization objectiveIterative processTarget samplesClassificationDomainLearningRetrainingAdaptationConvergenceUnsupervised 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-scaleSelf-Semantic Contour Adaptation for Cross Modality Brain Tumor Segmentation
Liu X, Xing F, Fakhri G, Woo J. Self-Semantic Contour Adaptation for Cross Modality Brain Tumor Segmentation. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2022, 00: 1-5. PMID: 35990931, PMCID: PMC9387767, DOI: 10.1109/isbi52829.2022.9761629.Peer-Reviewed Original ResearchUnsupervised domain adaptationAdaptive networkLow-level edge informationCross-domain alignmentEnhance segmentation performanceMulti-task frameworkCross-modality segmentationSegmentation of brain tumorsAdversarial learningDomain adaptationSemantic segmentationEdge informationSemantic alignmentPrecursor taskSegmentation performanceSpatial informationNetworkSemantic adaptationMagnetic resonance imagingTaskContour adaptationBraTS2018InformationFrameworkAdaptationDeep 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
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|xDomainSchemeClassificationMethodInferenceAdaptationAdapting 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 stageTaskFrameworkPrivacyDomainBraTSGenerative Self-training for Cross-Domain Unsupervised Tagged-to-Cine MRI Synthesis
Liu X, Xing F, Stone M, Zhuo J, Reese T, Prince J, El Fakhri G, Woo J. Generative Self-training for Cross-Domain Unsupervised Tagged-to-Cine MRI Synthesis. Lecture Notes In Computer Science 2021, 12903: 138-148. PMID: 34734217, PMCID: PMC8562649, DOI: 10.1007/978-3-030-87199-4_13.Peer-Reviewed Original ResearchUnsupervised domain adaptationTarget domainUDA methodsImage synthesisProblem of domain shiftUnsupervised domain adaptation frameworkSelf-trainingTraining deep learning modelsVariational Bayes learningUnlabeled target domainAlternating optimization schemePseudo-label selectionDeep learning modelsContinuous value predictionPseudo-labelsDomain adaptationDomain shiftCross-domainSynthesis qualityBayes learningDiscrete histogramsPrediction confidenceLearning modelsGeneration taskOptimization scheme
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