2023
Unsupervised Domain Adaptation by Cross-Prototype Contrastive Learning for Medical Image Segmentation
Cai Z, Xin J, Dong S, You C, Shi P, Zeng T, Zhang J, Onofrey J, Zheng N, Duncan J. Unsupervised Domain Adaptation by Cross-Prototype Contrastive Learning for Medical Image Segmentation. 2023, 00: 819-824. DOI: 10.1109/bibm58861.2023.10386055.Peer-Reviewed Original ResearchUnsupervised domain adaptationDistribution alignmentDomain adaptationContrastive learningUnsupervised domain adaptation methodsMedical image segmentation tasksDomain distribution alignmentGlobal distribution alignmentContrastive learning methodDomain adaptation performanceIntra-class distancePixel-level featuresImage segmentation tasksInter-class distancePublic cardiac datasetsCategory centroidDiscrimination of classesClass prototypesSegmentation taskSource domainTarget domainCardiac datasetsLearning methodsGlobal prototypesCentroid alignmentLiverHccSeg: A publicly available multiphasic MRI dataset with liver and HCC tumor segmentations and inter-rater agreement analysis
Gross M, Arora S, Huber S, Kücükkaya A, Onofrey J. LiverHccSeg: A publicly available multiphasic MRI dataset with liver and HCC tumor segmentations and inter-rater agreement analysis. Data In Brief 2023, 51: 109662. PMID: 37869619, PMCID: PMC10587725, DOI: 10.1016/j.dib.2023.109662.Peer-Reviewed Original ResearchTumor segmentation algorithmTumor segmentationSegmentation algorithmLiver segmentationManual segmentationTumor segmentation taskHigh-quality segmentationSegmentation taskSegmentation metricsSegmentation performanceAccurate segmentationRelevant metadataSegmentation agreementSegmentationMedical imagingFeature analysisExternal dataDatasetIntra-rater variabilityAlgorithmInnovative solutions
2022
Incremental 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
2018
Deep Learning Retinal Vessel Segmentation from a Single Annotated Example: An Application of Cyclic Generative Adversarial Neural Networks
Sadda P, Onofrey J, Papademetris X. Deep Learning Retinal Vessel Segmentation from a Single Annotated Example: An Application of Cyclic Generative Adversarial Neural Networks. Lecture Notes In Computer Science 2018, 11043: 82-91. DOI: 10.1007/978-3-030-01364-6_10.Peer-Reviewed Original ResearchGenerative adversarial neural networksAdversarial neural networkGround truth segmentationNeural networkTruth segmentationMedical image segmentation tasksImage segmentation tasksConvolutional neural networkDeep learning methodsRetinal vessel segmentationConvolutional networkSegmentation taskTraining examplesAnnotated examplesTraining dataLearning methodsVessel segmentationSegmentationSynthetic examplesNetworkLarge amountDatasetTaskExampleAccuracy