2024
Subject-aware PET Denoising with Contrastive Adversarial Domain Generalization
Liu X, Marin T, Eslahi S, Tiss A, Chemli Y, Johson K, Fakhri G, Ouyang J. Subject-aware PET Denoising with Contrastive Adversarial Domain Generalization. 2011 IEEE Nuclear Science Symposium Conference Record 2024, 00: 1-1. PMID: 39445307, PMCID: PMC11497478, DOI: 10.1109/nss/mic/rtsd57108.2024.10656150.Peer-Reviewed Original ResearchDomain generalizationDenoising performanceDenoising moduleDeep learningSubject-independent mannerSubject-invariant featuresSuperior denoising performanceAdversarial learning frameworkSubject-related informationConventional UNetBottleneck featuresTrustworthy systemsLearning frameworkDL modelsDL model performanceDenoisingNoise realizationsNegative samplesList-mode dataImage volumesModel performancePerformancePerformance of positron emission tomographyUNetFraction of events
2023
Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI
Liu X, Shih H, Xing F, Santarnecchi E, El Fakhri G, Woo J. Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI. Lecture Notes In Computer Science 2023, 14221: 46-56. PMID: 38665992, PMCID: PMC11045038, DOI: 10.1007/978-3-031-43895-0_5.Peer-Reviewed Original ResearchDeep learningDL modelsBrain tumor segmentation taskAbsence of training dataIncremental learning settingSegmenting various anatomical structuresBig medical dataInitial model trainingTumor segmentation taskBatch renormalizationCatastrophic forgettingIncremental learningSegmentation taskSource domainTraining dataModel trainingLearning structureSegmentation modelNetwork optimizationDiverse datasetsMedical dataEvolving environmentLearning settingsDistribution shiftsIncremental structureNoise-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 stagesRobustnessOutlier Robust Disease Classification via Stochastic Confidence Network
Lee K, Lee H, El Fakhri G, Sepulcre J, Liu X, Xing F, Hwang J, Woo J. Outlier Robust Disease Classification via Stochastic Confidence Network. Lecture Notes In Computer Science 2023, 14394: 80-90. DOI: 10.1007/978-3-031-47425-5_8.Peer-Reviewed Original ResearchDeep learningState-of-the-art modelsAccuracy of deep learningState-of-the-artMedical image dataMedical imaging modalitiesImage patchesIrrelevant patchesCategorical featuresPresence of outliersDL modelsConfidence networkConfidence predictionsClassifying outliersData samplesImage dataOutliersExperimental resultsDisease classificationImprove diagnostic performanceClassificationDiagnosing breast tumorsUltrasound imagingPerformanceImages