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 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
Self-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 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-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 schemeSubtype-aware Unsupervised Domain Adaptation for Medical Diagnosis
Liu X, Liu X, Hu B, Ji W, Xing F, Lu J, You J, Kuo C, Fakhri G, Woo J. Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis. Proceedings Of The AAAI Conference On Artificial Intelligence 2021, 35: 2189-2197. DOI: 10.1609/aaai.v35i3.16317.Peer-Reviewed Original Research