2025
Improved Vessel Segmentation with Symmetric Rotation-Equivariant U-Net
Zhang J, Du Y, Dvornek N, Onofrey J. Improved Vessel Segmentation with Symmetric Rotation-Equivariant U-Net. 2025, 00: 1-5. DOI: 10.1109/isbi60581.2025.10981208.Peer-Reviewed Original ResearchConvolutional neural networkU-NetModel sizeSmall memory costMedical image analysisU-Net architectureImproved vessel segmentationTrainable parametersMemory costComputer-Assisted InterventionSegmentation performanceNeural networkLearning methodsVessel segmentationLearning costLearning approachEquivariance propertyFundus imagesInconsistent predictionsAutomated SegmentationImage analysisPerformanceSegmentsImagesArchitecture
2024
Diabetic retinopathy data augmentation and vessel segmentation through deep learning based three fully convolution neural networks
Sachdeva J, Mishra P, Katoch D. Diabetic retinopathy data augmentation and vessel segmentation through deep learning based three fully convolution neural networks. Image And Vision Computing 2024, 151: 105284. DOI: 10.1016/j.imavis.2024.105284.Peer-Reviewed Original ResearchConvolutional neural networkNeural networkGaussian filterFundus imagesDifference of Gaussians filterGaussian (DoG) filterDifference of Gaussian (DoG) filterHigh frequency detailsEye fundus imagesVessel segmentation methodPresence of noiseFrequency detailsData augmentationAugmented imagesDeep learningVoting classifierVessel segmentationSegmentation methodFCNNEnsemble modelClinical datasetsNetworkRetinal fundusDatasetFilterCUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation
Liu C, Amodio M, Shen L, Gao F, Avesta A, Aneja S, Wang J, Del Priore L, Krishnaswamy S. CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation. Lecture Notes In Computer Science 2024, 15008: 155-165. DOI: 10.1007/978-3-031-72111-3_15.Peer-Reviewed Original ResearchMedical image segmentationImage segmentationLack of labeled dataUnsupervised deep learning frameworkSegmenting medical imagesDeep learning frameworkBrain MRI imagesRetinal fundus imagesContrastive learningLearning frameworkUnsupervised methodDeep learningExpert annotationsData topologyMedical imagesGranularity levelsEmbedding mapHausdorff distanceFundus imagesDice coefficientImage dataEmbeddingAnnotationLearningMRI images
2022
Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images Using Deep Learning
Lee J, Wanyan T, Chen Q, Keenan T, Glicksberg B, Chew E, Lu Z, Wang F, Peng Y. Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images Using Deep Learning. Lecture Notes In Computer Science 2022, 13583: 11-20. PMID: 36656604, PMCID: PMC9842432, DOI: 10.1007/978-3-031-21014-3_2.Peer-Reviewed Original ResearchLate age-related macular degenerationAge-related macular degenerationColor fundus photographsEye Disease StudyRisk prediction modelMacular degeneration progressionTriaging patientsFundus photographsPatient riskAMD cohortMacular degenerationPatient historyDegeneration progressionDisease StudyProgressionRiskSubsequent time intervalsPersonalized medicineAgeFundus imagesPatientsCohortDegenerationBaselinek-SALSA: k-Anonymous Synthetic Averaging of Retinal Images via Local Style Alignment
Jeon M, Park H, Kim H, Morley M, Cho H. k-SALSA: k-Anonymous Synthetic Averaging of Retinal Images via Local Style Alignment. Lecture Notes In Computer Science 2022, 13681: 661-678. PMID: 37525827, PMCID: PMC10388376, DOI: 10.1007/978-3-031-19803-8_39.Peer-Reviewed Original ResearchStyle alignmentMembership inference attacksRetinal imagesGenerative adversarial networkPotential of machineRetinal image analysisRetinal fundus imagesK-anonymityInference attacksPrivacy notionPrivate datasetAdversarial networkData sharingBenchmark datasetsTraining dataClassification performanceModern machineArt techniquesSource imagesImage fidelityFundus imagesPrior workVisual patternsImage analysisImages
2019
A multi-task deep learning model for the classification of Age-related Macular Degeneration.
Chen Q, Peng Y, Keenan T, Dharssi S, Agro N E, Wong W, Chew E, Lu Z. A multi-task deep learning model for the classification of Age-related Macular Degeneration. AMIA Joint Summits On Translational Science Proceedings 2019, 2019: 505-514. PMID: 31259005, PMCID: PMC6568069.Peer-Reviewed Original ResearchDeep learning modelsLearning modelMulti-task deep learning modelNovel deep learning modelMulti-task learning techniquesColor fundus imagesImage datasetsLearning techniquesAutomated classificationManual classificationArt modelsManual gradingFundus imagesGrading processClassificationImagesAge-related macular degenerationCurrent stateEye Disease Study GroupAMD severity scaleOverfittingDatasetMacular degenerationModelAccuracy
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