2024
Class-Aware Mutual Mixup with Triple Alignments for Semi-supervised Cross-Domain Segmentation
Cai Z, Xin J, Zeng T, Dong S, Zheng N, Duncan J. Class-Aware Mutual Mixup with Triple Alignments for Semi-supervised Cross-Domain Segmentation. Lecture Notes In Computer Science 2024, 15008: 68-79. DOI: 10.1007/978-3-031-72111-3_7.Peer-Reviewed Original ResearchSemi-supervised domain adaptationCross-domain segmentationTail classesBridge the domain gapState-of-the-art methodsMean-teacher modelUnlabeled target samplesLabeled source samplesState-of-the-artDomain gapDomain adaptationKnowledge distillationMixup strategyIntra-domainTarget domainEnhance model performanceMM-WHSData distributionSegmentation performanceTarget samplesMixupMS-CMRSegConsistency alignmentClass awarenessExperimental results
2023
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective.
You C, Dai W, Min Y, Liu F, Clifton D, Zhou S, Staib L, Duncan J. Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective. Advances In Neural Information Processing Systems 2023, 36: 9984-10021. PMID: 38813114, PMCID: PMC11136570.Peer-Reviewed Original ResearchMedical image segmentationContrastive learningImage segmentationSemi-supervised medical image segmentationSemi-supervised contrastive learningSelf-supervised objectiveSemantic segmentation datasetsSemi-supervised methodGround-truth labelsQuality of visual representationSafety-critical tasksSegmentation datasetTail classesSegmentation taskLabel setsTruth labelsCL frameworkNegative examplesModel collapseVariance-reductionVariance-reduction techniquesVisual representationTaskLearningPairs of samples
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply