2024
Accelerated 3D metabolite T1 mapping of the brain using variable‐flip‐angle SPICE
Zhao Y, Li Y, Guo R, Jin W, Sutton B, Ma C, Fakhri G, Li Y, Luo J, Liang Z. Accelerated 3D metabolite T1 mapping of the brain using variable‐flip‐angle SPICE. Magnetic Resonance In Medicine 2024, 92: 1310-1322. PMID: 38923032, DOI: 10.1002/mrm.30200.Peer-Reviewed Original ResearchConceptsLow-rank tensor modelGeneralized series modelMetabolite TExperimental resultsBrain metabolitesClinically acceptable scan timeEfficient encodingPhantom experimental resultsAcceptable scan timeNoisy dataSparse samplingImaging problemsData processingHealthy subject dataVariable flip angleFlip angleTensor modelSaturation effectsQuantitative metabolic imagingMRSI techniquePhantomScan timeData acquisitionMetabolic imagingT1 mappingEvaluation of few-shot detection of head and neck anatomy in CT
Lee K, Cho J, Lee J, Xing F, Liu X, Bae H, Lee K, Hwang J, Park J, Fakhri G, Jee K, Woo J. Evaluation of few-shot detection of head and neck anatomy in CT. Progress In Biomedical Optics And Imaging 2024, 12927: 1292716-1292716-7. DOI: 10.1117/12.3006895.Peer-Reviewed Original ResearchFew-shot object detection methodMedical image dataFew-shot object detectionObject detection methodsObject detectionImage dataObject detection approachState-of-the-artFaster R-CNNFine-tuning stageDeep learning modelsDetection methodFew-shotDetection of anatomical structuresDownstream tasksNatural imagesR-CNNDetect objectsDetection headDetection approachPreprocessing stepDetect anatomical structuresLearning modelsExperimental resultsClinical workflowDisentangled multimodal brain MR image translation via transformer-based modality infuser
Cho J, Liu X, Xing F, Ouyang J, Fakhri G, Park J, Woo J. Disentangled multimodal brain MR image translation via transformer-based modality infuser. Progress In Biomedical Optics And Imaging 2024, 12926: 129262h-129262h-6. DOI: 10.1117/12.3006502.Peer-Reviewed Original ResearchConvolutional neural networkBrain tumor segmentation taskModality-specific featuresTumor segmentation taskImage translationAdversarial networkSegmentation taskSynthesis qualityBrain MR imagesNeural networkMR modalitiesAcquired imagesExperimental resultsNetworkGlobal relationshipsDisease diagnosisImagesEncodingBraTSDatasetFeaturesTaskMethodSuperiorityMR imagingSubtype-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
TauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent Diffusion Models
Jang S, Gomez C, Thibault E, Becker J, Dong Y, Normandin M, Price J, Johnson K, Fakhri G, Gong K. TauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent Diffusion Models. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338710.Peer-Reviewed Original ResearchFine-Tuning Network in Federated Learning for Personalized Skin Diagnosis
Lee K, Lee H, Cavalcanti T, Kim S, El Fakhri G, Lee D, Woo J, Hwang J. Fine-Tuning Network in Federated Learning for Personalized Skin Diagnosis. Lecture Notes In Computer Science 2023, 14222: 378-388. DOI: 10.1007/978-3-031-43898-1_37.Peer-Reviewed Original ResearchFederated learningSkin disease diagnosisMobile devicesState-of-the-art approachesUtilization of mobile devicesFine-tuning networkState-of-the-artFine-tuning methodAbstract Federated learningDeep learning networkField of medical diagnosisDeep networksLearning networkAdaptive mannerModified GADisease diagnosisGenetic algorithmSuperior performanceMedical diagnosisArchitectural designClinical datasetsExperimental resultsNetworkModel designPersonality diversitySpeech Audio Synthesis from Tagged MRI and Non-negative Matrix Factorization via Plastic Transformer
Liu X, Xing F, Stone M, Zhuo J, Fels S, Prince J, El Fakhri G, Woo J. Speech Audio Synthesis from Tagged MRI and Non-negative Matrix Factorization via Plastic Transformer. Lecture Notes In Computer Science 2023, 14226: 435-445. PMID: 38651032, PMCID: PMC11034915, DOI: 10.1007/978-3-031-43990-2_41.Peer-Reviewed Original ResearchWeight mapAudio waveformEnd-to-end deep learning frameworkMatrix factorization-based approachesFactorization-based approachDeep learning frameworkNon-negative matrix factorizationEnd-to-endAdversarial trainingProcess of speech productionTwo-dimensional spectrogramConventional convolutionLearning frameworkMotion featuresTraining samplesAudio synthesisDimension expansionMatrix inputMatrix factorizationTagged MRISpeech productionTransformation modelExperimental resultsSpectrogramPlastic transformationNoise-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
2022
Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translator
Liu X, Xing F, Prince J, Zhuo J, Stone M, El Fakhri G, Woo J. Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translator. Lecture Notes In Computer Science 2022, 13436: 376-386. PMID: 36820764, PMCID: PMC9942274, DOI: 10.1007/978-3-031-16446-0_36.Peer-Reviewed Original ResearchAudio waveformEnd-to-end deep learning frameworkAdversarial training approachDeep learning frameworkEnd-to-endTwo-dimensional spectrogramAdversarial networkIntermediate representationLearning frameworkResidual attentionDisentanglement strategyAudio synthesisDataset sizeImprove realismHeterogeneous representationsHeterogeneous translationAttentional strategiesTraining approachExperimental resultsMuscle deformationIntelligible speechMotor control theoriesTagged-MRIRelated-disordersSpeech acousticsCmri2spec: Cine MRI Sequence to Spectrogram Synthesis via A Pairwise Heterogeneous Translator
Liu X, Xing F, Stone M, Prince J, Kim J, El Fakhri G, Woo J. Cmri2spec: Cine MRI Sequence to Spectrogram Synthesis via A Pairwise Heterogeneous Translator. 2013 IEEE International Conference On Acoustics, Speech And Signal Processing 2022, 00: 1481-1485. PMID: 36212702, PMCID: PMC9544268, DOI: 10.1109/icassp43922.2022.9746381.Peer-Reviewed Original ResearchMultimodal representation learningAdversarial training approachCine MRI sequencesCNN encoderRepresentation learningConvolutional decoderAdversarial networkDataset sizeSequence encodingEnhance realismHeterogeneous translationSpeech wordsSynthesis frameworkTraining approachExperimental resultsEncodingSynthesis accuracySpectrogramFrameworkDecodingAdversarial Non-local Multi-modality MRI Aggregation for Directional DWI Synthesis
Liu X, Xing F, Wedeen V, Fakhri G, Woo J. Adversarial Non-local Multi-modality MRI Aggregation for Directional DWI Synthesis. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2022 DOI: 10.58530/2022/3449.Peer-Reviewed Original ResearchVariational inference for quantifying inter-observer variability in segmentation of anatomical structures
Liu X, Xing F, Marin T, Fakhri G, Woo J. Variational inference for quantifying inter-observer variability in segmentation of anatomical structures. Proceedings Of SPIE--the International Society For Optical Engineering 2022, 12032: 120321m-120321m-6. PMID: 36303579, PMCID: PMC9603619, DOI: 10.1117/12.2604547.Peer-Reviewed Original ResearchSegmentation mapImage dataVariational inference frameworkVariational autoencoder networkMedical image dataInherent information lossMagnetic Resonance (MRSegmentation datasetAutoencoder networkVariational inferenceLatent vectorsInformation lossManual annotationSegmentation methodAleatoric uncertaintyInference frameworkSacrificing accuracyExperimental resultsAnnotationOrgan boundariesInter-observer variabilityImagesELBOMapsSegmentsVoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI
Liu X, Xing F, Yang C, Kuo C, Babu S, Fakhri G, Jenkins T, Woo J. VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI. IEEE Journal Of Biomedical And Health Informatics 2022, 26: 1128-1139. PMID: 34339378, PMCID: PMC8807766, DOI: 10.1109/jbhi.2021.3097735.Peer-Reviewed Original ResearchConceptsConvolutional neural networkLearning modelsDimension reductionSubspace learning modelConcatenation of featuresState-of-the-artUnsupervised dimension reductionDeep learning modelsMedical image dataSupervised dimension reductionImage dataClassification of amyotrophic lateral sclerosisSubspace learningClassification taskDeep learningDataset sizeNeural networkSubspace approximationMemory requirementsTraining datasetClassification approachAUC scoreAccurate classificationDatasetExperimental results
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|xDomainSchemeClassificationMethodInferenceAdaptation
2020
Identifying the Common and Subject-specific Functional Units of Speech Movements via a Joint Sparse Non-negative Matrix Factorization Framework.
Woo J, Xing F, Prince J, Stone M, Reese T, Wedeen V, El Fakhri G. Identifying the Common and Subject-specific Functional Units of Speech Movements via a Joint Sparse Non-negative Matrix Factorization Framework. Proceedings Of SPIE--the International Society For Optical Engineering 2020, 11313 PMID: 32454553, PMCID: PMC7243345.Peer-Reviewed Original Research