Featured Publications
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 ResearchMeSH KeywordsAnisotropyBayes TheoremHumansImage Processing, Computer-AssistedLearningReproducibility of ResultsUncertaintyConceptsUnsupervised 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
2023
Noise-Robust Sleep Staging via Adversarial Training With an Auxiliary Model
Yoo C, Liu X, Xing F, Fakhri G, Woo J, Kang J. Noise-Robust Sleep Staging via Adversarial Training With an Auxiliary Model. IEEE Transactions On Biomedical Engineering 2023, 70: 1252-1263. PMID: 36227815, DOI: 10.1109/tbme.2022.3214269.Peer-Reviewed Original ResearchConceptsProblem of domain shiftTarget networkConsumer-level devicesPre-trained modelsSleep staging performanceSevere noise levelsEfficient training approachAdversarial trainingAdversarial noiseDomain shiftAdversarial transformationsAuxiliary modelDL modelsInput perturbationsTraining modelArbitrary noiseTesting stageEnhanced robustnessNoise signalsTraining approachTest environmentExperimental resultsNoiseSleep stagesRobustness
2021
Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas
Marin T, Zhuo Y, Lahoud R, Tian F, Ma X, Xing F, Moteabbed M, Liu X, Grogg K, Shusharina N, Woo J, Lim R, Ma C, Chen Y, El Fakhri G. Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas. Radiotherapy And Oncology 2021, 167: 269-276. PMID: 34808228, PMCID: PMC8934266, DOI: 10.1016/j.radonc.2021.09.034.Peer-Reviewed Original ResearchMeSH KeywordsDeep LearningHumansObserver VariationRadiotherapy Planning, Computer-AssistedReproducibility of ResultsSarcomaSoft Tissue NeoplasmsConceptsGross tumor volumeRadiation therapy treatment planningGross tumor volume contoursGross tumor volume delineationTherapy treatment planningIntra-observer variabilityConsensus contoursGTV contoursPre-operative CT imagesSoft tissue sarcomasRadiation oncologistsTumor volumeBone sarcomasTreatment planningAccurate contoursCT imagesDelineation procedureSarcomaSoft tissueConfidence levelRadiationPatientsHausdorff distanceMultiple contoursX-ray