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 fundusDatasetFilter
2021
Magnetic resonance parameter mapping using model‐guided self‐supervised deep learning
Liu F, Kijowski R, Fakhri G, Feng L. Magnetic resonance parameter mapping using model‐guided self‐supervised deep learning. Magnetic Resonance In Medicine 2021, 85: 3211-3226. PMID: 33464652, PMCID: PMC9185837, DOI: 10.1002/mrm.28659.Peer-Reviewed Original ResearchConceptsMR parameter mappingSupervised learningReconstruction qualityImaging modelSelf-supervised deep learningStandard supervised learningConventional iterative reconstructionData setsDeep learning purposesSuperior reconstruction qualityImprove reconstruction qualityQuantitative MRI applicationsUndersampled k-spacePresence of noisePhysical modeling constraintsSparsity constraintNetwork trainingReconstruction performanceDeep learningReconstruction frameworkMap extractionImprove image qualitySuppress noiseGround truthUndersampling artifacts
2013
Interstitial fluid glucose time-lag correction for real-time continuous glucose monitoring
Keenan D, Mastrototaro J, Weinzimer S, Steil G. Interstitial fluid glucose time-lag correction for real-time continuous glucose monitoring. Biomedical Signal Processing And Control 2013, 8: 81-89. DOI: 10.1016/j.bspc.2012.05.007.Peer-Reviewed Original Research
2008
Calculation of the confidence intervals for transformation parameters in the registration of medical images
Bansal R, Staib LH, Laine AF, Xu D, Liu J, Posecion LF, Peterson BS. Calculation of the confidence intervals for transformation parameters in the registration of medical images. Medical Image Analysis 2008, 13: 215-233. PMID: 19138877, PMCID: PMC2891652, DOI: 10.1016/j.media.2008.09.002.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsArtificial IntelligenceConfidence IntervalsCorpus CallosumData Interpretation, StatisticalHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance ImagingPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueConceptsSimilarity transformationMultivariate GaussianLeast squares estimationTransformation parametersMathematical frameworkRandom variablesPresence of noiseCovariance matrixLandmark pointsQuantifying errorsSimilarity parameterAmount of misregistrationInherent technological limitationsAmount of noiseGaussianCoordinatesInevitable errorsReal-world datasetsFunctional relationAmount of blurErrorParametersWorld datasetsNoiseConfidence intervals
2006
Detecting Coupling in the Presence of Noise and Nonlinearity
Netoff T, Carroll T, Pecora L, Schiff S. Detecting Coupling in the Presence of Noise and Nonlinearity. 2006, 265-282. DOI: 10.1002/9783527609970.ch11.Peer-Reviewed Original Research
2005
Semi-blind ICA of fMRI: A method for utilizing hypothesis-derived time courses in a spatial ICA analysis
Calhoun VD, Adali T, Stevens MC, Kiehl KA, Pekar JJ. Semi-blind ICA of fMRI: A method for utilizing hypothesis-derived time courses in a spatial ICA analysis. NeuroImage 2005, 25: 527-538. PMID: 15784432, DOI: 10.1016/j.neuroimage.2004.12.012.Peer-Reviewed Original ResearchConceptsPrior informationIndependent component analysisData analysis applicationsSemi-blind independent component analysisData-driven approachThree-stimulus auditory oddball paradigmTemporal constraintsAnalysis applicationsICA analysisTime-course informationFlexible wayPresence of noiseCourse informationRegression approachSimulation resultsBlind source separation techniqueExplicit constraintsFlexible modeling approachFMRI dataSource separation techniquesInformationUseful applicationsHigher order statistical momentsModeling approachNegative associates
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