2025
Computer-based Tracking of Microsurgical Instruments - A Novel Assessment Tool for Robotic-assisted and Conventional Microsurgery.
Stögner V, Wessel K, Xie X, Wong A, Yu C, Boroumand S, Huelsboemer L, Pomahac B, Kueckelhaus M, Ayyala H. Computer-based Tracking of Microsurgical Instruments - A Novel Assessment Tool for Robotic-assisted and Conventional Microsurgery. Plastic & Reconstructive Surgery 2025 PMID: 40558078, DOI: 10.1097/prs.0000000000012271.Peer-Reviewed Original Research
2024
Deep-Reticular Pseudodrusen-Net: A 3-Dimensional Deep Network for Detection of Reticular Pseudodrusen on OCT Scans
Elsawy A, Keenan T, Thavikulwat A, Lu A, Bellur S, Mukherjee S, Agron E, Chen Q, Chew E, Lu Z. Deep-Reticular Pseudodrusen-Net: A 3-Dimensional Deep Network for Detection of Reticular Pseudodrusen on OCT Scans. Ophthalmology Science 2024, 5: 100655. PMID: 39866344, PMCID: PMC11758204, DOI: 10.1016/j.xops.2024.100655.Peer-Reviewed Original ResearchSemi-supervised learningReticular pseudodrusenOCT scansRetina specialistsOptical coherence tomographyArea under ROC curveSpectral-domain optical coherence tomographyBaseline modelOptical coherence tomography scansAge-related macular degeneration studyDetect reticular pseudodrusenFundus autofluorescence imagingDeep learning networkDeep networksBaseline methodsPretrained modelsModel decision-makingReading centerLearning networkHigh-performance metricsOCT studiesTomography scanAREDS2En faceCoherence tomographyAn Efficient Contrastive Unimodal Pretraining Method for EHR Time Series Data
King R, Kodali S, Krueger C, Yang T, Mortazavi B. An Efficient Contrastive Unimodal Pretraining Method for EHR Time Series Data. 2024, 00: 1-8. DOI: 10.1109/bhi62660.2024.10913624.Peer-Reviewed Original ResearchDeep neural networksElectronic health recordsMachine learningSelf-supervised taskSelf-supervised learningSemi-supervised learningEffective feature extractionMIMIC-III datasetExtract meaningful informationTimeseries dataExtract valuable insightsSOTA methodsContrastive pretrainingLabeled dataFeature extractionNeural networkData batchesEICU datasetTime series dataMeaningful informationMIMIC-IIILinear evaluationComplex mappingComputational demandsHealth recordsOptimal and Safe Estimation for High-Dimensional Semi-Supervised Learning
Deng S, Ning Y, Zhao J, Zhang H. Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning. Journal Of The American Statistical Association 2024, 119: 2748-2759. PMID: 40078670, PMCID: PMC11902906, DOI: 10.1080/01621459.2023.2277409.Peer-Reviewed Original ResearchSemi-supervised estimatorConditional mean functionMean functionSupervised estimationParameters of linear modelsSemi-supervised learningRegression parametersEstimation problemLinear modelSupplementary materialsTheoretical resultsParameter estimationSemi-supervised settingUnlabeled dataLabeled dataEstimationMinimaxMisspecificationNumerical simulationsDataFunctionLearningProblemData analysis
2023
UPCoL: Uncertainty-Informed Prototype Consistency Learning for Semi-supervised Medical Image Segmentation
Lu W, Lei J, Qiu P, Sheng R, Zhou J, Lu X, Yang Y. UPCoL: Uncertainty-Informed Prototype Consistency Learning for Semi-supervised Medical Image Segmentation. Lecture Notes In Computer Science 2023, 14223: 662-672. DOI: 10.1007/978-3-031-43901-8_63.Peer-Reviewed Original ResearchSemi-supervised learningMedical image segmentationUnlabeled dataConsistency learningImage segmentationState-of-the-art SSL methodsSemi-supervised medical image segmentationPrototype representationSemi-supervised segmentationState-of-the-artSSL methodsConsistency constraintsUnlabeled regionsDatasetLearningRepresentationPrototypeEmbeddingSegmentsFrameworkConstraints
2022
ACT: Semi-supervised Domain-Adaptive Medical Image Segmentation with Asymmetric Co-training
Liu X, Xing F, Shusharina N, Lim R, Jay Kuo C, El Fakhri G, Woo J. ACT: Semi-supervised Domain-Adaptive Medical Image Segmentation with Asymmetric Co-training. Lecture Notes In Computer Science 2022, 13435: 66-76. PMID: 36780245, PMCID: PMC9911133, DOI: 10.1007/978-3-031-16443-9_7.Peer-Reviewed Original ResearchSemi-supervised domain adaptationUnsupervised domain adaptationSemi-supervised learningMedical image segmentationDomain adaptationDomain shiftLabel supervisionTarget domainImage segmentationDomain dataLeverage different knowledgePseudo-label noiseSignificant domain shiftSupervised joint trainingLabeled source domainUnlabeled target dataUnlabeled target domainLabeled target samplesTarget domain dataSource domain dataState-of-the-artMRI segmentation taskSubstantial performance gainsPseudo-labelsLabel noise
2015
Laplacian SVM Based Feature Selection Improves Medical Event Reports Classification
Fodeh S, Miller P, Brandt C, Benin A, Lee K, Koss M. Laplacian SVM Based Feature Selection Improves Medical Event Reports Classification. 2015, 449-454. DOI: 10.1109/icdmw.2015.141.Peer-Reviewed Original Research
2014
Semi-supervised Learning of Nonrigid Deformations for Image Registration
Onofrey J, Staib L, Papademetris X. Semi-supervised Learning of Nonrigid Deformations for Image Registration. Lecture Notes In Computer Science 2014, 8331: 13-23. DOI: 10.1007/978-3-319-05530-5_2.Peer-Reviewed Original ResearchLarge medical image databasesSemi-supervised learning frameworkMedical image databasesStatistical deformation modelSemi-supervised learningIntensity-based registrationImage databaseLeave-one-out cross validationLearning frameworkSupervised registrationsMR datasetsImage registrationRegistration algorithmUnsupervised registrationsBrain imagesNonrigid transformationNonrigid deformationResearch communityBrain Atlas databaseVast quantitiesCross validationMR brainRegistrationLarge amountDatabase
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