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
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|xDomainSchemeClassificationMethodInferenceAdaptationGenerative 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
2020
Severity-Aware Semantic Segmentation with Reinforced Wasserstein Training
Liu X, Ji W, You J, Fakhri G, Woo J. Severity-Aware Semantic Segmentation with Reinforced Wasserstein Training. 2020, 00: 12563-12572. DOI: 10.1109/cvpr42600.2020.01258.Peer-Reviewed Original ResearchSemantic segmentationCARLA simulatorCross-entropyGround distance matrixWasserstein training frameworkAlternating optimization schemeCityscapes datasetDNN architecturesCE lossTraining frameworkSemantic classesGround metricInter-class correlationAutonomous vehiclesSuperior performanceOptimization schemeDNNCARLADistance matrixSurgery systemCamVidDeepLabSimulationCityscapesPixel