2024
Medical image registration via neural fields
Sun S, Han K, You C, Tang H, Kong D, Naushad J, Yan X, Ma H, Khosravi P, Duncan J, Xie X. Medical image registration via neural fields. Medical Image Analysis 2024, 97: 103249. PMID: 38963972, DOI: 10.1016/j.media.2024.103249.Peer-Reviewed Original ResearchLearning-based methodsNeural fieldsNeural networkImage registrationMedical image analysis tasksMini-batch gradient descentImage analysis tasksDeep neural networksMedical image registrationDiffeomorphic image registrationImage registration frameworkOptimization-based methodDomain shiftAnalysis tasksGradient descentCompetitive performanceImage pairsRegistration taskOptimal deformationShort computation timeRegistration frameworkDesign choicesDisplacement vector fieldComputation timeModel optimizationSpectral Brain Graph Neural Network for Prediction of Anxiety in Children with Autism Spectrum Disorder
Duan P, Dvornek N, Wang J, Eilbott J, Du Y, Sukhodolsky D, Duncan J. Spectral Brain Graph Neural Network for Prediction of Anxiety in Children with Autism Spectrum Disorder. 2024, 00: 1-5. DOI: 10.1109/isbi56570.2024.10635753.Peer-Reviewed Original ResearchGraph neural networksFunctional magnetic resonance imagingAutism spectrum disorderNeural networkCurrent graph neural networksSpectrum disorderMASC-2Spectral analysis algorithmAnalysis algorithmGraph-based networkMultidimensional Anxiety ScaleFast Fourier transformPredictive of anxietyDaily anxiety levelsExtract hidden informationBrain functional networksPower spectrum densityNode featuresNetwork performanceComorbid anxietyBrain mechanismsHidden informationCorrelated featuresAnxiety ScaleTotal score
2021
BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis
Li X, Zhou Y, Dvornek N, Zhang M, Gao S, Zhuang J, Scheinost D, Staib LH, Ventola P, Duncan JS. BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis. Medical Image Analysis 2021, 74: 102233. PMID: 34655865, PMCID: PMC9916535, DOI: 10.1016/j.media.2021.102233.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagesGraph neural network frameworkMedical image analysisGraph neural networkGraph convolutional layersNeural network frameworkDifferent evaluation metricsSpecific task statesIndependent fMRI datasetsPooling layerConvolutional layersConsistency lossNetwork frameworkNeural networkFMRI datasetsImage analysis methodEvaluation metricsDetection resultsBrain graphsSubjects releaseROI selectionImage analysisCognitive stimuliTask statesFMRI analysisShape-Regularized Unsupervised Left Ventricular Motion Network With Segmentation Capability In 3d+ Time Echocardiography
Ta K, Ahn SS, Stendahl JC, Sinusas AJ, Duncan JS. Shape-Regularized Unsupervised Left Ventricular Motion Network With Segmentation Capability In 3d+ Time Echocardiography. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2021, 00: 536-540. PMID: 34168721, PMCID: PMC8221369, DOI: 10.1109/isbi48211.2021.9433888.Peer-Reviewed Original ResearchConvolutional neural networkAccurate motion estimationCardiac motion patternsMotion estimation performanceDense displacement fieldB-mode echocardiography imagesSegmentation masksMedical imagesMotion estimationNeural networkSegmentation capabilityTarget imageUnsupervised estimationImportant taskSegmentationMotion patternsDisplacement fieldNetworkEchocardiography imagesEstimation performanceImagesLow signalAdditional challengesMotion networkNoise ratio
2020
Layer Embedding Analysis in Convolutional Neural Networks for Improved Probability Calibration and Classification
Zhang F, Dvornek N, Yang J, Chapiro J, Duncan J. Layer Embedding Analysis in Convolutional Neural Networks for Improved Probability Calibration and Classification. IEEE Transactions On Medical Imaging 2020, 39: 3331-3342. PMID: 32356739, PMCID: PMC7606489, DOI: 10.1109/tmi.2020.2990625.Peer-Reviewed Original ResearchConceptsConvolutional neural networkNeural networkClassification taskProbability calibrationTissue classification tasksImage representationBaseline methodsPublic datasetsModel performanceRandom forest modelNetworkBetter performanceForest modelDatasetClassificationTaskCT imagesImagesOriginal model outputMR imagesModel outputInstitutional datasetPerformanceEmbeddingOutputDemographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity
Dvornek NC, Li X, Zhuang J, Ventola P, Duncan JS. Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity. Lecture Notes In Computer Science 2020, 12436: 363-372. PMID: 34308438, PMCID: PMC8299434, DOI: 10.1007/978-3-030-59861-7_37.Peer-Reviewed Original ResearchRecurrent neural network modelRecurrent neural networkNeural network modelFunctional magnetic resonance imaging (fMRI) time series dataAttention mechanismArt resultsNeural networkCross-validation frameworkNetwork modelTime series dataIndividual demographic informationABIDE IImproved classificationNetwork differencesNetworkClassificationFunctional network differencesFrameworkIndividual demographic variablesInformationUnsupervised motion tracking of left ventricle in echocardiography
Ahn SS, Ta K, Lu A, Stendahl JC, Sinusas AJ, Duncan JS. Unsupervised motion tracking of left ventricle in echocardiography. Proceedings Of SPIE--the International Society For Optical Engineering 2020, 11319: 113190z-113190z-7. PMID: 32994659, PMCID: PMC7521020, DOI: 10.1117/12.2549572.Peer-Reviewed Original ResearchMotion trackingGround truth displacement fieldsConvolutional neural networkAccurate motion trackingDense displacement fieldB-mode echocardiography imagesU-NetNeural networkTracking frameworkNon-rigid registration algorithmTarget imageRegistration algorithmTarget frameSource frameAlgorithmEchocardiography imagesFavorable performanceDatasetImagesTrackingDisplacement estimationLarge amountEchocardiographic imagesSegmentationNetwork
2019
Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI
Dvornek NC, Li X, Zhuang J, Duncan JS. Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI. Lecture Notes In Computer Science 2019, 11861: 382-390. PMID: 32274470, PMCID: PMC7143657, DOI: 10.1007/978-3-030-32692-0_44.Peer-Reviewed Original ResearchRecurrent neural networkAutism Brain Imaging Data ExchangeFunctional magnetic resonance imaging (fMRI) dataLarge fMRI datasetShort-term memory structureGenerative modelLong short-term memory (LSTM) structureGenerative recurrent neural networkNeural networkLSTM nodesFMRI time series dataTime series dataMagnetic resonance imaging dataRNN-based modelsNetwork interpretabilityFMRI datasetsMemory structureClassification taskData exchangeDiscriminative modelGraph Neural Network for Interpreting Task-fMRI Biomarkers
Li X, Dvornek NC, Zhou Y, Zhuang J, Ventola P, Duncan JS. Graph Neural Network for Interpreting Task-fMRI Biomarkers. Lecture Notes In Computer Science 2019, 11768: 485-493. PMID: 32984866, PMCID: PMC7519579, DOI: 10.1007/978-3-030-32254-0_54.Peer-Reviewed Original ResearchDeep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features
Wang CJ, Hamm CA, Savic LJ, Ferrante M, Schobert I, Schlachter T, Lin M, Weinreb JC, Duncan JS, Chapiro J, Letzen B. Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features. European Radiology 2019, 29: 3348-3357. PMID: 31093705, PMCID: PMC7243989, DOI: 10.1007/s00330-019-06214-8.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAlgorithmsBile Duct NeoplasmsBile Ducts, IntrahepaticCarcinoma, HepatocellularCholangiocarcinomaDeep LearningFemaleHumansImage Interpretation, Computer-AssistedLiver NeoplasmsMachine LearningMagnetic Resonance ImagingMaleMiddle AgedNeural Networks, ComputerPredictive Value of TestsProof of Concept StudyRetrospective StudiesConceptsDeep learning systemConvolutional neural networkLearning systemRelevance scoresFeature mapsPre-trained CNN modelsFeature relevance scoresMulti-phasic MRINeural network interpretationEvidence-based decision supportDeep NeuralDeep learningCNN modelLesion classifierLearning prototypeNeural networkOriginal imageSystem prototypeDecision supportLesion classificationNetwork interpretationImage voxelsIncorrect featuresLesion classesTest set
2018
Liver Tissue Classification Using an Auto-context-based Deep Neural Network with a Multi-phase Training Framework
Zhang F, Yang J, Nezami N, Laage-gaupp F, Chapiro J, De Lin M, Duncan J. Liver Tissue Classification Using an Auto-context-based Deep Neural Network with a Multi-phase Training Framework. Lecture Notes In Computer Science 2018, 11075: 59-66. PMID: 32432233, PMCID: PMC7236808, DOI: 10.1007/978-3-030-00500-9_7.Peer-Reviewed Original ResearchNeural networkNovel deep convolutional neural networkStandard neural network approachesTraining frameworkDeep convolutional neural networkU-Net-like architectureTissue classificationConvolutional neural networkDeep neural networksNeural network approachSegmentation masksBenchmark methodsNetwork approachPatch-based strategyLearning spacesLiver tissue classificationMagnetic resonance imagesPromising resultsNetworkImagesPredictive modelClassificationFrameworkResonance imagesArchitectureLearning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets
Dvornek NC, Yang D, Ventola P, Duncan JS. Learning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets. Lecture Notes In Computer Science 2018, 11072: 329-337. PMID: 30873514, PMCID: PMC6411297, DOI: 10.1007/978-3-030-00931-1_38.Peer-Reviewed Original ResearchConceptsRecurrent neural networkNeural networkTask fMRI datasetsMedical image analysis problemsSuch deep networksImage analysis problemsTask fMRI scanTypical control subjectsDeep networkDeep learningTraining lossSmall datasetsLarge datasetsNumber of approachesAutism spectrum disorderAnalysis problemDatasetNetworkTraining runsImage analysisGeneralizable modelNon-imaging variablesSpectrum disorderFMRI analysisModel performanceBrain Biomarker Interpretation in ASD Using Deep Learning and fMRI
Li X, Dvornek NC, Zhuang J, Ventola P, Duncan JS. Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI. Lecture Notes In Computer Science 2018, 11072: 206-214. PMID: 32984865, PMCID: PMC7519581, DOI: 10.1007/978-3-030-00931-1_24.Peer-Reviewed Original ResearchDeep neural networksFunctional magnetic resonance imagingBrain functional magnetic resonance imagingAutism spectrum disorderComputer visionDeep learningDNN classifierMagnetic resonance imagingCorrupt imagesNeural networkSaliency featuresClassification scenariosNeurological functionControl subjectsComplex neurodevelopmental disorderEarly diagnosisComputational decisionReliable biomarkersResonance imagingBiomarker interpretationBiomarkersDetected biomarkersNeurodevelopmental disordersBrain featuresClassifierCombining Phenotypic and Resting-State FMRI Data for Autism Classification with Recurrent Neural Networks
Dvornek NC, Ventola P, Duncan JS. Combining Phenotypic and Resting-State FMRI Data for Autism Classification with Recurrent Neural Networks. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2018, 2018: 725-728. PMID: 30288208, PMCID: PMC6166875, DOI: 10.1109/isbi.2018.8363676.Peer-Reviewed Original ResearchAutism spectrum disorderRecurrent neural networkNeural networkAutism Brain Imaging Data ExchangeSingle deep learning frameworkHeterogeneity of ASDFunctional magnetic resonance imagingDeep learning frameworkResting-state fMRI dataResting-state functional magnetic resonance imagingBetter classification accuracyAutism classificationSpectrum disorderData exchangeLearning frameworkFMRI dataClassification accuracyCross-validation frameworkChallenging taskStraightforward taskPrior workNetworkSuch dataRsfMRITask2-Channel Convolutional 3D Deep Neural Network (2CC3D) for FMRI Analysis: ASD Classification and Feature Learning
Li X, Dvornek NC, Papademetris X, Zhuang J, Staib LH, Ventola P, Duncan JS. 2-Channel Convolutional 3D Deep Neural Network (2CC3D) for FMRI Analysis: ASD Classification and Feature Learning. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2018, 2018: 1252-1255. PMID: 32983370, PMCID: PMC7519578, DOI: 10.1109/isbi.2018.8363798.Peer-Reviewed Original ResearchConvolutional neural networkNeural networkCNN convolutional layerSpatial featuresASD classificationDeep neural networksMean F-scoreTraditional machineFeature learningConvolutional layersInput formatF-scoreClassification modelTemporal informationNetworkWindow parametersImagesClassificationConvolutionalTemporal statisticsMachineLearningFeaturesFormatScheme