2022
Flow-Based Visual Quality Enhancer for Super-Resolution Magnetic Resonance Spectroscopic Imaging
Dong S, Hangel G, Chen E, Sun S, Bogner W, Widhalm G, You C, Onofrey J, de Graaf R, Duncan J. Flow-Based Visual Quality Enhancer for Super-Resolution Magnetic Resonance Spectroscopic Imaging. Lecture Notes In Computer Science 2022, 13609: 3-13. DOI: 10.1007/978-3-031-18576-2_1.Peer-Reviewed Original ResearchAdversarial networkVisual qualityDeep learning-based super-resolution methodsLearning-based super-resolution methodsFlow-based modelImage visual qualityGenerative adversarial networkHigh visual qualitySuper-resolution methodSuper-resolved imagesGenerative modelHigh-resolution imagesImage modalitiesFlow-based methodNetworkLow spatial resolutionUncertainty estimationImagesPromising resultsEnhancer networkAnatomical informationHigh fidelityEssential toolDatasetQuality adjustmentIncremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation
You C, Xiang J, Su K, Zhang X, Dong S, Onofrey J, Staib L, Duncan J. Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation. Lecture Notes In Computer Science 2022, 13573: 3-16. PMID: 37415747, PMCID: PMC10323962, DOI: 10.1007/978-3-031-18523-6_1.Peer-Reviewed Original ResearchIncremental learningMedical image segmentation tasksMulti-site datasetImage segmentation tasksMedical image segmentationProstate MRI SegmentationComputation resourcesMedical datasetsSegmentation taskImage segmentationSegmentation frameworkEmbedding featuresBenchmark datasetsMRI segmentationTraining dataTarget domainLearning approachPractical deploymentDomain-specific expertiseCompetitive performanceDatasetTraining schemePrior workSegmentationSingle model
2021
Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer
Zhou B, Zhou S, Duncan JS, Liu C. Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer. IEEE Transactions On Medical Imaging 2021, 40: 1792-1804. PMID: 33729929, PMCID: PMC8325575, DOI: 10.1109/tmi.2021.3066318.Peer-Reviewed Original ResearchConceptsAttention networkView reconstructionGrand challenge datasetLimited angle reconstructionHigh-quality reconstructionNeural network methodSparse-view reconstructionExperimental resultsLimited angle acquisitionArchitecture issuesSparse viewsChallenge datasetLimited view dataView dataNeural architectureQuality reconstructionNetwork methodTomographic reconstructionReconstructed imagesProjection viewsPrevious methodsAngle reconstructionDatasetNetworkAngle acquisition
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 datasetPerformanceEmbeddingOutputUnsupervised 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
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 performance