2025
Artificial intelligence-assisted detection of nasopharyngeal carcinoma on endoscopic images: a national, multicentre, model development and validation study
Shi Y, Li Z, Wang L, Wang H, Liu X, Gu D, Chen X, Liu X, Gong W, Jiang X, Li W, Lin Y, Liu K, Luo D, Peng T, Peng X, Tong M, Zheng H, Zhou X, Wu J, El Fakhri G, Chang M, Liao J, Li J, Wang D, Ye J, Qu S, Jiang W, Liu Q, Sun X, Zheng Y, Yu H. Artificial intelligence-assisted detection of nasopharyngeal carcinoma on endoscopic images: a national, multicentre, model development and validation study. The Lancet Digital Health 2025, 100869. PMID: 40544083, DOI: 10.1016/j.landig.2025.03.001.Peer-Reviewed Original ResearchArea under the curveNasopharyngeal carcinomaEndoscopic imagesReal-world environmentDeep learning algorithmsDeep learning systemBenign hyperplasiaNormal nasopharynxShanghai Municipal Key Clinical SpecialtyLearning algorithmsMalignant imagesSkull base tumorsDetection of nasopharyngeal carcinomaLearning systemAI modelsIncidence of nasopharyngeal carcinomaPrimary hospitalsDiagnostic capabilitiesNasopharyngeal carcinoma diagnosisDiagnostic challengeImaging manifestationsCarcinoma diagnosisCarcinomaDiagnostic accuracyEndoscopic examinationModeling inter‐reader variability in clinical target volume delineation for soft tissue sarcomas using diffusion model
Dong Y, Marin T, Zhuo Y, Najem E, Beddok A, Rozenblum L, Moteabbed M, Grogg K, Xing F, Woo J, Chen Y, Lim R, Liu X, Ma C, Fakhri G. Modeling inter‐reader variability in clinical target volume delineation for soft tissue sarcomas using diffusion model. Medical Physics 2025 PMID: 40317577, DOI: 10.1002/mp.17865.Peer-Reviewed Original ResearchClinical target volumeGross tumor volumeClinical target volume delineationSoft tissue sarcomasInter-reader variabilityTissue sarcomasClinical target volume contoursMagnetic resonance imagingCTV delineationTarget volume delineationComputed tomographyTreatment of soft tissue sarcomasFluorodeoxyglucose positron emission tomographyCTV contoursTarget volumeVolume delineationT1-weighted magnetic resonance imagingRadiotherapy treatmentEnergy distanceHigh Dice indexPositron emission tomographyTumor volumeMicroscopic spreadFDG-PETTreatment planningDual Prompting for Diverse Count-Level Pet Denoising
Liu X, Huang Y, Marin T, Chemli Y, Eslahi S, Tiss A, Johnson K, Fakhri G, Ouyang J. Dual Prompting for Diverse Count-Level Pet Denoising. 2025, 00: 1-5. DOI: 10.1109/isbi60581.2025.10980695.Peer-Reviewed Original ResearchGross tumor volume confidence maps prediction for soft tissue sarcomas from multi-modality medical images using a diffusion model
Dong Y, Marin T, Zhuo Y, Najem E, Moteabbed M, Xing F, Beddok A, Lahoud R, Rozenblum L, Ding Z, Liu X, Grogg K, Woo J, Chen Y, Lim R, Ma C, Fakhri G. Gross tumor volume confidence maps prediction for soft tissue sarcomas from multi-modality medical images using a diffusion model. Physics And Imaging In Radiation Oncology 2025, 33: 100734. PMID: 40123775, PMCID: PMC11926426, DOI: 10.1016/j.phro.2025.100734.Peer-Reviewed Original ResearchGross tumor volumeSoft tissue sarcomasTissue sarcomasGross tumor volume delineationManual GTV delineationsMagnetic resonance imagingComputed tomographyFluorodeoxyglucose positron emission tomographyGTV delineationT1-weighted magnetic resonance imagingSingle-modePositron emission tomographyMulti-modal medical imagesTumor volumeIntra-reader variabilityFDG-PETTreatment planningSarcomaEmission tomographyImaging modalitiesResonance imagingDiffusion modelDice indexReader variabilityPatients
2024
Diffusion-based Bayesian posterior distribution prediction of kinetic parameters in dynamic PET
Djebra Y, Liu X, Marin T, Tiss A, Dhaynaut M, Guehl N, Johnson K, Fakhri G, Ma C, Ouyang J. Diffusion-based Bayesian posterior distribution prediction of kinetic parameters in dynamic PET. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657955.Peer-Reviewed Original ResearchConditional variational autoencoderEfficient deep learning-based approachMarkov chain Monte CarloDenoising diffusion probabilistic modelDeep learning-based approachDiffusion probabilistic modelLearning-based approachApproximate posterior distributionPosterior distributionVariational autoencoderHeavy computationTau protein aggregationBayesian inferenceProbabilistic modelData-drivenStudy molecular processesBayesian posterior distributionProtein aggregationMetropolis-Hastings Markov chain Monte CarloMolecular processesAlzheimer's diseaseNeurodegenerative diseasesKinetic parametersEstimate posterior distributionsAutoencoderAblation Study of Diffusion Model with Transformer Backbone for Low-count PET Denoising
Huang Y, Liu X, Miyazaki T, Omachi S, Fakhri G, Ouyang J. Ablation Study of Diffusion Model with Transformer Backbone for Low-count PET Denoising. 2011 IEEE Nuclear Science Symposium Conference Record 2024, 00: 1-2. PMID: 39445309, PMCID: PMC11497477, DOI: 10.1109/nss/mic/rtsd57108.2024.10655179.Peer-Reviewed Original ResearchIR tasksImage restorationImage super-resolution taskField of image restorationSuper-resolution taskLatent feature spaceConventional UNetDenoising iterationDenoising taskTransformer backboneDenoising autoencoderTexture restorationVision transformerFeature spaceAblation studiesLearning schemeBackbone networkImage generationDenoisingUNetIR modelPSNRSpatial informationAutoencoderTaskSubject-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 eventsPoint-supervised Brain Tumor Segmentation with Box-prompted Medical Segment Anything Model
Liu X, Woo J, Ma C, Ouyang J, Fakhri G. Point-supervised Brain Tumor Segmentation with Box-prompted Medical Segment Anything Model. 2011 IEEE Nuclear Science Symposium Conference Record 2024, 00: 1-1. PMID: 39445308, PMCID: PMC11497479, DOI: 10.1109/nss/mic/rtsd57108.2024.10656071.Peer-Reviewed Original ResearchInvestigating muscle coordination patterns with Granger causality analysis in protrusive motion from tagged and diffusion MRI
Park H, Xing F, Stone M, Kang H, Liu X, Zhuo J, Fels S, Reese T, Wedeen V, Fakhri G, Prince J, Woo J. Investigating muscle coordination patterns with Granger causality analysis in protrusive motion from tagged and diffusion MRI. JASA Express Letters 2024, 4: 095201. PMID: 39240196, PMCID: PMC11384280, DOI: 10.1121/10.0028500.Peer-Reviewed Original ResearchExploring 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 modelDiffusion Model-Based Posterior Distribution Prediction for Kinetic Parameter Estimation in Dynamic PET
Djebra Y, Liu X, Marin T, Tiss A, Dhaynaut M, Guehl N, Johnson K, Fakhri G, Ma C, Ouyang J. Diffusion Model-Based Posterior Distribution Prediction for Kinetic Parameter Estimation in Dynamic PET. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2024, 00: 1-5. PMID: 39530051, PMCID: PMC11554386, DOI: 10.1109/isbi56570.2024.10635805.Peer-Reviewed Original ResearchPosterior distributions of kinetic parametersDenoising diffusion probabilistic modelHyperphosphorylated tauP-tauDiffusion probabilistic modelAlzheimer's diseaseNeurodegenerative diseasesKinetic parametersPosterior distributionInference efficiencyComputational needsEstimate kinetic parametersProbabilistic modelComputation timeCross noise level PET denoising with continuous adversarial domain generalization
Liu X, Eslahi S, Marin T, Tiss A, Chemli Y, Huang Y, Johnson K, Fakhri G, Ouyang J. Cross noise level PET denoising with continuous adversarial domain generalization. Physics In Medicine And Biology 2024, 69: 085001. PMID: 38484401, PMCID: PMC11195012, DOI: 10.1088/1361-6560/ad341a.Peer-Reviewed Original ResearchDomain generalization techniqueDomain generalizationDenoising performanceSuperior denoising performanceLatent feature representationGeneral techniqueDistribution shiftsAdversarial trainingDenoised imageFeature representationDomain labelsDistribution divergenceNoise levelDeep learningImage spaceDenoisingPerformance degradationCore ideaNoise realizationsCD methodNoiseImage volumesPerformanceImagesPSNRDisentangled 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 imagingThe role of 18F-FDG PET in minimizing variability in gross tumor volume delineation of soft tissue sarcomas
Najem E, Marin T, Zhuo Y, Lahoud R, Tian F, Beddok A, Rozenblum L, Xing F, Moteabbed M, Lim R, Liu X, Woo J, Lostetter S, Lamane A, Chen Y, Ma C, El Fakhri G. The role of 18F-FDG PET in minimizing variability in gross tumor volume delineation of soft tissue sarcomas. Radiotherapy And Oncology 2024, 194: 110186. PMID: 38412906, PMCID: PMC11042980, DOI: 10.1016/j.radonc.2024.110186.Peer-Reviewed Original ResearchGross tumor volume delineationGross tumor volumeDice similarity coefficientF-FDG PET imagingSoft tissue sarcomasInter-reader variabilityGTV delineationRadiation therapy treatment planningF-FDGF-FDG PETTherapy treatment planningPerformance level estimationTumor volume delineationTissue sarcomasPET imagingVolume delineationSimultaneous truthHausdorff distanceDice similarity coefficient scoreAccurate gross tumor volumeImaging modality groupsWilcoxon signed-rank testStatistically significant decreaseSigned-rank testTumor volumeSubtype-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 dataDomainLabelingTreatment-wise glioblastoma survival Inference with multi-parametric preoperative MRI
Liu X, Shusharina N, Shih H, Kuo C, El Fakhri G, Woo J. Treatment-wise glioblastoma survival Inference with multi-parametric preoperative MRI. Proceedings Of SPIE--the International Society For Optical Engineering 2024, 12927: 129272h-129272h-5. PMID: 39444513, PMCID: PMC11497473, DOI: 10.1117/12.3006897.Peer-Reviewed Original ResearchSpeech motion anomaly detection via cross-modal translation of 4D motion fields from tagged MRI
Liu X, Xing F, Zhuo J, Stone M, Prince J, El Fakhri G, Woo J. Speech motion anomaly detection via cross-modal translation of 4D motion fields from tagged MRI. Proceedings Of SPIE--the International Society For Optical Engineering 2024, 12926: 129262w-129262w-5. PMID: 39238547, PMCID: PMC11377028, DOI: 10.1117/12.3006874.Peer-Reviewed Original ResearchCross-modal translationHealthy individualsTongue cancer patientsMotion fieldOut-of-distributionOne-class SVMPatient dataAnomaly detectionAnomaly detectorCancer patientsTagged MRISpeech-related disordersGeneralization capabilityReconstruction qualitySpeech qualityArticulatory-acoustic relationsPatientsSpeech waveformTraining setInnovative treatmentsMRITest setMotion patternsArticulatory featuresTraining translators
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 structureSpeech 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 transformationAttentive 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
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