2022
Federated Transfer Learning for Low-Dose PET Denoising: A Pilot Study With Simulated Heterogeneous Data
Zhou B, Miao T, Mirian N, Chen X, Xie H, Feng Z, Guo X, Li X, Zhou S, Duncan J, Liu C. Federated Transfer Learning for Low-Dose PET Denoising: A Pilot Study With Simulated Heterogeneous Data. IEEE Transactions On Radiation And Plasma Medical Sciences 2022, 7: 284-295. PMID: 37789946, PMCID: PMC10544830, DOI: 10.1109/trpms.2022.3194408.Peer-Reviewed Original ResearchLow-dose PETMedical data privacy regulationsFederated learning algorithmLarge domain shiftTransfer learning frameworkData privacy regulationsHigh-quality reconstructionFederated transferData privacyHeterogeneous dataDomain shiftLearning frameworkLearning algorithmPrivacy regulationsData distributionCollaborative trainingLow-dose dataPET reconstructionPrevious methodsFL methodEfficient wayLocal dataSuperior performanceExperimental resultsDenoisingSimultaneous Segmentation and Motion Estimation of Left Ventricular Myocardium in 3D Echocardiography Using Multi-task Learning
Ta K, Ahn SS, Stendahl JC, Langdon J, Sinusas AJ, Duncan JS. Simultaneous Segmentation and Motion Estimation of Left Ventricular Myocardium in 3D Echocardiography Using Multi-task Learning. Lecture Notes In Computer Science 2022, 13131: 123-131. PMID: 35759335, PMCID: PMC9221412, DOI: 10.1007/978-3-030-93722-5_14.Peer-Reviewed Original ResearchMotion estimationMulti-task learning networkMedical image analysis literatureMulti-task learningSingle-task learningMotion estimation techniqueImage analysis literatureComputer visionDecoding branchesFeature encoderLearning frameworkLearning networkLatent featuresAccurate segmentationSimultaneous segmentationEstimate motionImage pairsTask learningRealistic motion patternsVolumetric segmentationSegmentationMotion patternsTaskUnique taskLearning
2019
Domain-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 modalitiesTaskSegmentationEmbeddingLearning
2018
Combining 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 dataRsfMRITask