2024
A Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging
Dong S, Cai Z, Hangel G, Bogner W, Widhalm G, Huang Y, Liang Q, You C, Kumaragamage C, Fulbright R, Mahajan A, Karbasi A, Onofrey J, de Graaf R, Duncan J. A Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging. Medical Image Analysis 2024, 99: 103358. PMID: 39353335, DOI: 10.1016/j.media.2024.103358.Peer-Reviewed Original ResearchDenoising diffusion modelsDeep learning-based super-resolution methodsLearning-based super-resolution methodsMulti-scale super-resolutionGenerative modelSuper-resolution methodsDeep learning modelsHigh-resolution magnetic resonance spectroscopic imagingHigh-quality imagesPost-processing approachSuper-resolutionFlow-based networksLearning modelsLow resolutionTruncation stepLow-resolution dataSharpness adjustmentNetworkSensitivity restrictionsUncertainty estimationDiffusion modelImagesCapabilitySampling processSpectroscopic imaging
2020
Efficient Shapley Explanation for Features Importance Estimation Under Uncertainty
Li X, Zhou Y, Dvornek NC, Gu Y, Ventola P, Duncan JS. Efficient Shapley Explanation for Features Importance Estimation Under Uncertainty. Lecture Notes In Computer Science 2020, 12261: 792-801. PMID: 34308439, PMCID: PMC8299327, DOI: 10.1007/978-3-030-59710-8_77.Peer-Reviewed Original ResearchShapley value explanationMedical image dataDeep learning modelsFeature importance estimationImage dataLearning modelComplex deep learning modelsImportance estimationFeature importance resultsShapley value frameworkInput feature importancePublic neuroimaging datasetComputational complexityShapley explanationsFeature importanceUncertainty estimation methodExplanation workParticular predictionNeuroimaging datasetsMedical fieldImportance resultsImpressive powerEstimation methodUnderlying propertiesImportant approachMulti-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results
Li X, Gu Y, Dvornek N, Staib LH, Ventola P, Duncan JS. Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results. Medical Image Analysis 2020, 65: 101765. PMID: 32679533, PMCID: PMC7569477, DOI: 10.1016/j.media.2020.101765.Peer-Reviewed Original ResearchConceptsDeep learning modelsFederated LearningPrivacy-preserving federated learningLearning modelFederated learning approachPrivacy-preserving strategyDomain adaptation methodsData analysis problemsLocal model weightsIterative optimization algorithmEntity dataDomain adaptationLearning approachLearning formulationMulti-site dataRandomization mechanismAdaptation methodNeuroimage analysisDifferent tasksModel weightsModel optimizationOptimization algorithmPrivate informationTraining strategyAnalysis problem
2019
Invertible Network for Classification and Biomarker Selection for ASD
Zhuang J, Dvornek NC, Li X, Ventola P, Duncan JS. Invertible Network for Classification and Biomarker Selection for ASD. Lecture Notes In Computer Science 2019, 11766: 700-708. PMID: 32274471, PMCID: PMC7144624, DOI: 10.1007/978-3-030-32248-9_78.Peer-Reviewed Original ResearchInvertible networksDeep learning methodsDeep learning modelsBlack-box natureLowest regression errorRegression tasksClassification taskLearning methodsLearning modelDecision boundariesModel decisionsImportant edgesLinear classifierConnectivity matrixASD classificationNetworkBlack-box representationBiomarker selectionRegression errorsData pointsImportance measuresTaskNovel methodClassificationClassifierUnsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation
Yang J, Dvornek NC, Zhang F, Chapiro J, Lin M, Duncan JS. Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation. Lecture Notes In Computer Science 2019, 11765: 255-263. PMID: 32377643, PMCID: PMC7202929, DOI: 10.1007/978-3-030-32245-8_29.Peer-Reviewed Original ResearchDice similarity coefficientDomain adaptationContent spaceDomain shiftTarget domainCross-modality domain adaptationUnsupervised domain adaptation methodsDiverse image generationLiver segmentation taskDeep learning modelsDifferent target domainUnlabeled target dataFeature-level informationUnsupervised domain adaptationDomain adaptation methodsMulti-phasic MRISegmentation taskSegmentation performanceSegmentation modelImage generationLiver segmentationStyle transferDisentangled representationsBetter generalizationSource domainDomain-Agnostic Learning with Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation
Yang J, Dvornek NC, Zhang F, Zhuang J, Chapiro J, Lin M, Duncan JS. Domain-Agnostic Learning with Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation. ICCV Workshops 2019, 00: 323-331. PMID: 34676308, PMCID: PMC8528125, DOI: 10.1109/iccvw.2019.00043.Peer-Reviewed Original ResearchDomain adaptationDisentangled representationsLiver segmentationTarget domainSource domainDeep learning modelsGenerative adversarial networkHuman interpretabilityLearning frameworkAdversarial networkDownstream tasksArt methodsSegmentation consistencyLearning modelAgnostic learningMeaningful representationCycleGANNew tasksAblation analysisDA taskDifferent modalitiesTaskSegmentationEmbeddingLearningEfficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery
Li X, Dvornek NC, Zhou Y, Zhuang J, Ventola P, Duncan JS. Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery. Lecture Notes In Computer Science 2019, 11492: 718-730. PMID: 32982121, PMCID: PMC7519580, DOI: 10.1007/978-3-030-20351-1_56.Peer-Reviewed Original ResearchShapley value explanationAutism spectrum disorderFunctional magnetic resonance imagingDeep learning modelsDeep learning classifierCooperative game theoryLearning modelLearning classifiersGraph structureRandom forestGame theoryMachine learning methodsMNIST datasetTraditional learning strategiesSpectrum disorderFMRI biomarkersComputational complexityLearning methodsHuman perceptionHierarchical pipelineFeature importanceN featuresLearning strategiesInput dataEfficient interpretation