2025
Mixture-of-Shape-Experts (MoSE): End-to-End Shape Dictionary Framework to Prompt SAM for Generalizable Medical Segmentation
Wei J, Zhao X, Woo J, Ouyang J, Fakhri G, Chen Q, Liu X. Mixture-of-Shape-Experts (MoSE): End-to-End Shape Dictionary Framework to Prompt SAM for Generalizable Medical Segmentation. 2025, 00: 6450-6460. DOI: 10.1109/cvprw67362.2025.00642.Peer-Reviewed Original ResearchSingle domain generalizationEnd-to-endShape priorsGeneralization capabilityEnd-to-end mannerDictionary learning methodMedical image segmentationMixture-of-expertsMultiple public datasetsShape mapsDictionary atomsDictionary learningDictionary sizeShape dictionaryRepresentational powerDomain generalizationPublic datasetsGating networkImage segmentationMedical segmentationLearning methodsShape informationDictionaryBidirectional integrationOverfitting
2024
Style mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation
Cai Z, Xin J, You C, Shi P, Dong S, Dvornek N, Zheng N, Duncan J. Style mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation. Medical Image Analysis 2024, 101: 103440. PMID: 39764933, DOI: 10.1016/j.media.2024.103440.Peer-Reviewed Original ResearchConceptsUnsupervised domain adaptationMedical image segmentationDomain-invariant representationsImage segmentationDomain adaptationDisentanglement learningImage translationUnsupervised domain adaptation approachState-of-the-art methodsDomain shift problemDomain-invariant learningState-of-the-artPublic cardiac datasetsDiverse constraintsAdversarial learningConsistency regularizationContrastive learningFeature spaceSemantic consistencyComprehensive experimentsDomain generalizationData diversityShift problemMedical segmentationCardiac datasets
2023
Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts
You C, Dai W, Min Y, Staib L, Duncan J. Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts. Lecture Notes In Computer Science 2023, 14222: 561-571. PMID: 38840671, PMCID: PMC11151725, DOI: 10.1007/978-3-031-43898-1_54.Peer-Reviewed Original ResearchMedical image segmentationImage segmentationSegmentation methodPixel-level featuresComputer graphics problemImplicit neural representationsGrid-based representationMedical segmentationRendering frameworkSegmentation predictionsEnd mannerCorrelated contentCompetitive performance improvementsGraphics problemsSegmentationPoint representationPerformance improvementRegular gridSuch informationRepresentationConvolution operatorsExpertsComplex signalsRenderingFeaturesVicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation
Huang Y, Xie W, Li M, Cheng M, Wu J, Wang W, You J, Liu X. Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation. Lecture Notes In Computer Science 2023, 13939: 360-371. DOI: 10.1007/978-3-031-34048-2_28.Peer-Reviewed Original ResearchFederated LearningData augmentationFeature statisticsDeep learningAvailability of labeled dataPerformance of FLPrivacy protectionGeneralization capabilityData distributionMedical segmentationGaussian prototypeCollaborative trainingFL methodsFeature shiftsMinimization perspectiveAugmented scopeRaw dataCardiac segmentsGaussian distributionVolume segmentationData biasDiscrepancy of dataDataLearningMixup
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply