2024
Patient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep Learning
Pak D, Liu M, Kim T, Liang L, Caballero A, Onofrey J, Ahn S, Xu Y, McKay R, Sun W, Gleason R, Duncan J. Patient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep Learning. IEEE Transactions On Medical Imaging 2024, 43: 203-215. PMID: 37432807, PMCID: PMC10764002, DOI: 10.1109/tmi.2023.3294128.Peer-Reviewed Original ResearchConceptsFinite element analysisDeep learning methodsSpatial accuracyElement analysisDeep learningStress estimationLearning methodsSimulation accuracyDeployment simulationHigh spatial accuracyThin structuresMesh generationVolumetric meshingDeformation energyGeometry modelingVolumetric meshMesh qualityElement qualitySimultaneous optimizationMain noveltyBiomechanics studiesMeshModeling characteristicsAccuracyDownstream analysis
2021
Distortion Energy for Deep Learning-Based Volumetric Finite Element Mesh Generation for Aortic Valves
Pak D, Liu M, Kim T, Liang L, McKay R, Sun W, Duncan J. Distortion Energy for Deep Learning-Based Volumetric Finite Element Mesh Generation for Aortic Valves. Lecture Notes In Computer Science 2021, 12906: 485-494. DOI: 10.1007/978-3-030-87231-1_47.Peer-Reviewed Original ResearchFinite element analysisVolumetric meshFinite element methodMesh generation techniqueFinite element mesh generationWatertight surface meshesSurface meshElement analysisElement methodStress analysisMeshing techniqueClosure simulationHexahedral elementsGood spatial accuracyDeformation frameworkMesh generationElement qualityOffset operationAccurate generationDeep learningSpatial accuracyDistortion energyMeshModeling strategyGeneration technique
2020
Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation
Onofrey JA, Staib LH, Huang X, Zhang F, Papademetris X, Metaxas D, Rueckert D, Duncan JS. Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation. Annual Review Of Biomedical Engineering 2020, 22: 1-27. PMID: 32169002, PMCID: PMC9351438, DOI: 10.1146/annurev-bioeng-060418-052147.Peer-Reviewed Original Research
2019
Deep 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
Learning 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 featuresClassifier