2023
FedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising
Zhou B, Xie H, Liu Q, Chen X, Guo X, Feng Z, Hou J, Zhou S, Li B, Rominger A, Shi K, Duncan J, Liu C. FedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising. Medical Image Analysis 2023, 90: 102993. PMID: 37827110, PMCID: PMC10611438, DOI: 10.1016/j.media.2023.102993.Peer-Reviewed Original ResearchConceptsFederated learning processFederated learning algorithmFederated learning strategyLarge domain shiftDifferent data distributionsTransformation networkLarge-scale datasetsDeep learningDomain shiftLearning algorithmDownstream tasksNetwork weightsFeature outputFeature transformationSecurity concernsData distributionCollaborative trainingPersonalized modelPET image qualityReconstructed imagesReconstruction methodImage qualityNetworkEfficient wayLocal data
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 resultsDenoising