2025
Texture and noise dual adaptation for infrared image super-resolution
Huang Y, Miyazaki T, Liu X, Dong Y, Omachi S. Texture and noise dual adaptation for infrared image super-resolution. Pattern Recognition 2025, 163: 111449. DOI: 10.1016/j.patcog.2025.111449.Peer-Reviewed Original ResearchTexture detailsAdversarial lossSuper-resolutionInfrared image super-resolutionVisible imagesImage super-resolutionState-of-the-artIR image qualityVisible light imagesAdversarial trainingExtraction branchUpsampling factorsBlurring artifactsImage processingModel adaptationAdaptive approachSpatial domainImage qualityNoiseInnovation frameworkLight imagesNoise transferDual adaptationImagesTexture distributionThe Dyson equalizer: adaptive noise stabilization for low-rank signal detection and recovery
Landa B, Kluger Y. The Dyson equalizer: adaptive noise stabilization for low-rank signal detection and recovery. Information And Inference A Journal Of The IMA 2025, 14: iaae036. PMID: 39830802, PMCID: PMC11735832, DOI: 10.1093/imaiai/iaae036.Peer-Reviewed Original ResearchNoise varianceLow-rank signalData matrixNoisy data matrixSignal detectionImproved signal recoverySignal recoveryMP lawNoise distributionRandom matrix theory resultsSignal structureData-drivenSingular valuesNoiseDataTaskNoise characteristicsNoise levelNoise stabilitySingle-cell RNA sequencingDetectionSpatial transcriptomics dataTranscriptome dataNormalization procedureMatrixBiomedRAG: A retrieval augmented large language model for biomedicine
Li M, Kilicoglu H, Xu H, Zhang R. BiomedRAG: A retrieval augmented large language model for biomedicine. Journal Of Biomedical Informatics 2025, 162: 104769. PMID: 39814274, PMCID: PMC11837810, DOI: 10.1016/j.jbi.2024.104769.Peer-Reviewed Original Research
2024
The impact of functional correlations on task information coding
Ito T, Murray J. The impact of functional correlations on task information coding. Network Neuroscience 2024, 8: 1331-1354. PMID: 39735511, PMCID: PMC11675092, DOI: 10.1162/netn_a_00402.Peer-Reviewed Original ResearchNoise correlationsTrial-to-trialBrain networksFunctional brain networksFunctional correlatesFMRI networksSignal correlationNeural correlatesBrain regionsInformation codingTask selectionFMRI datasetsTask informationNeural unitsCorrelated changesCoding frameworkBrainInvestigate relationshipsNetworkFMRINoiseCross noise level PET denoising with continuous adversarial domain generalization
Liu X, Eslahi S, Marin T, Tiss A, Chemli Y, Huang Y, Johnson K, Fakhri G, Ouyang J. Cross noise level PET denoising with continuous adversarial domain generalization. Physics In Medicine And Biology 2024, 69: 085001. PMID: 38484401, PMCID: PMC11195012, DOI: 10.1088/1361-6560/ad341a.Peer-Reviewed Original ResearchDomain generalization techniqueDomain generalizationDenoising performanceSuperior denoising performanceLatent feature representationGeneral techniqueDistribution shiftsAdversarial trainingDenoised imageFeature representationDomain labelsDistribution divergenceNoise levelDeep learningImage spaceDenoisingPerformance degradationCore ideaNoise realizationsCD methodNoiseImage volumesPerformanceImagesPSNRPrediction Consistency Regularization for Learning with Noise Labels Based on Contrastive Clustering
Sun X, Zhang S, Ma S. Prediction Consistency Regularization for Learning with Noise Labels Based on Contrastive Clustering. Entropy 2024, 26: 308. PMID: 38667864, PMCID: PMC11049179, DOI: 10.3390/e26040308.Peer-Reviewed Original ResearchLabel noiseContrastive clusteringConsistency regularizationRegularization termPrediction consistencyClassification accuracyImpact of label noiseEffects of label noiseClassification taskClustering problemComprehensive experimentsNoise labelsLabel informationNeural networkClustering resultsSample recognitionNoise rateMitigate noiseNoiseClassificationModel performanceRegularizationPrototypeAccuracyLabelingBayesian Spectral Graph Denoising with Smoothness Prior
Leone S, Sun X, Perlmutter M, Krishnaswamy S. Bayesian Spectral Graph Denoising with Smoothness Prior. 2024, 00: 1-6. DOI: 10.1109/ciss59072.2024.10480177.Peer-Reviewed Original ResearchPresence of noisy dataGraph signal processingMaximum A PosterioriAffinity graphDenoised featuresGaussian noiseNoisy dataHigh-dimensionalComplex dataAlgorithm's abilityA-posterioriModel of noise generationSmoothness priorsRestored signalDistributed noiseSignal processingAlgorithmImage dataGraphFrequency domainNoiseNoise generationDenoisingWhite noiseSmoothing
2023
Dose reduction in dynamic synaptic vesicle glycoprotein 2A PET imaging using artificial neural networks
Li A, Yang B, Naganawa M, Fontaine K, Toyonaga T, Carson R, Tang J. Dose reduction in dynamic synaptic vesicle glycoprotein 2A PET imaging using artificial neural networks. Physics In Medicine And Biology 2023, 68: 245006. PMID: 37857316, PMCID: PMC10739622, DOI: 10.1088/1361-6560/ad0535.Peer-Reviewed Original ResearchSource-free domain adaptive segmentation with class-balanced complementary self-training
Huang Y, Xie W, Li M, Xiao E, You J, Liu X. Source-free domain adaptive segmentation with class-balanced complementary self-training. Artificial Intelligence In Medicine 2023, 146: 102694. PMID: 38042612, DOI: 10.1016/j.artmed.2023.102694.Peer-Reviewed Original ResearchUnsupervised domain adaptationPseudo-label noiseSelf-trainingSegmentation taskSource domainUnsupervised domain adaptation methodsBrain tumor segmentation taskDomain adaptive segmentationLabeled source domainPatient data privacyTumor segmentation taskDomain adaptationData privacyLabeled dataTarget domainSelection schemeAdaptive segmentationSource dataExperimental resultsMinor categoriesTaskNoiseIntellectual propertyPrivacySegmentorDetection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices
Khunte A, Sangha V, Oikonomou E, Dhingra L, Aminorroaya A, Mortazavi B, Coppi A, Brandt C, Krumholz H, Khera R. Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices. Npj Digital Medicine 2023, 6: 124. PMID: 37433874, PMCID: PMC10336107, DOI: 10.1038/s41746-023-00869-w.Peer-Reviewed Original ResearchArtificial intelligenceRandom Gaussian noiseNoisy electrocardiogramGaussian noiseElectrocardiogram (ECGWearable devicesSingle-lead electrocardiogramPortable devicesSNRWearableNoiseDevice noiseRepositoryAI-based screeningIntelligenceDetectionDevicesNoise sourcesVentricular systolic dysfunctionModelElectrocardiogramSingle-lead electrocardiographyTrainingSVD Compression for Nonlinear Encoding Imaging with Model-based Deep Learning Reconstruction
Zhang Z, Selvaganesan K, Ha Y, Sun C, Samardzija A, Sun H, Galiana G, Constable R. SVD Compression for Nonlinear Encoding Imaging with Model-based Deep Learning Reconstruction. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2023 DOI: 10.58530/2023/0833.Peer-Reviewed Original ResearchDeep learning reconstructionModel-based networkLearning reconstructionEncoded imageGPU memoryRegularization termEncoding matrixGraph nodesGaussian noiseSimulated Gaussian noiseBloch-SiegertEncoding dimensionEncodingSVDModel partPhysical model partNetworkGPUMR scannerNonlinear caseNodesRedundancyReconstructionCompressionNoiseNoise-Robust Sleep Staging via Adversarial Training With an Auxiliary Model
Yoo C, Liu X, Xing F, Fakhri G, Woo J, Kang J. Noise-Robust Sleep Staging via Adversarial Training With an Auxiliary Model. IEEE Transactions On Biomedical Engineering 2023, 70: 1252-1263. PMID: 36227815, DOI: 10.1109/tbme.2022.3214269.Peer-Reviewed Original ResearchConceptsProblem of domain shiftTarget networkConsumer-level devicesPre-trained modelsSleep staging performanceSevere noise levelsEfficient training approachAdversarial trainingAdversarial noiseDomain shiftAdversarial transformationsAuxiliary modelDL modelsInput perturbationsTraining modelArbitrary noiseTesting stageEnhanced robustnessNoise signalsTraining approachTest environmentExperimental resultsNoiseSleep stagesRobustness
2022
A Noise-Level-Aware Framework for PET Image Denoising
Li Y, Cui J, Chen J, Zeng G, Wollenweber S, Jansen F, Jang S, Kim K, Gong K, Li Q. A Noise-Level-Aware Framework for PET Image Denoising. Lecture Notes In Computer Science 2022, 13587: 75-83. DOI: 10.1007/978-3-031-17247-2_8.Peer-Reviewed Original ResearchDeep convolutional neural networkPET image denoisingImage denoisingConvolutional neural networkDenoising frameworkDenoising operationBaseline methodsDenoising needsLocal noise levelBackbone networkPatient PET imagesNeural networkDenoisingNoise levelScanner sensitivityPET/CT systemNetworkPET imagingNoise-levelEmbeddingImage acquisition durationAcquisition durationAdministered activityImagesNoise
2021
Predicting individual neuron responses with anatomically constrained task optimization
Mano O, Creamer MS, Badwan BA, Clark DA. Predicting individual neuron responses with anatomically constrained task optimization. Current Biology 2021, 31: 4062-4075.e4. PMID: 34324832, PMCID: PMC8741219, DOI: 10.1016/j.cub.2021.06.090.Peer-Reviewed Original ResearchConceptsArtificial networksInference problemStatistical representationTask optimizationSmall neural networksNeural networkNoise constraintsMotion detection modelArtificial neural networkBiological circuitsMotion detectorsModelNetworkPropertiesOptimizationConstraintsIndividual neuron responsesDetectorNoiseProblemNeuron propertiesCircuitCovariance distributions in single particle tracking
Bailey MLP, Yan H, Surovtsev I, Williams JF, King MC, Mochrie SGJ. Covariance distributions in single particle tracking. Physical Review E 2021, 103: 032405. PMID: 33862686, PMCID: PMC9115892, DOI: 10.1103/physreve.103.032405.Peer-Reviewed Original ResearchConceptsCovariance distributionLocalization noiseMultivariate Gaussian random variablesGaussian random variablesSkew-normal distributionStatistical propertiesDisplacement covarianceRandom variablesThird central momentHeterogeneous noiseAnomalous diffusionProbability distributionTheory-experiment discrepancyViscoelastic polymer solutionsCentral momentsParticle trajectoriesTheoretical meansTheoretical equationsMotionSingle modeRecent experimentsTheoryNoiseParticle trackingCovarianceDoubly Stochastic Normalization of the Gaussian Kernel Is Robust to Heteroskedastic Noise.
Landa B, Coifman RR, Kluger Y. Doubly Stochastic Normalization of the Gaussian Kernel Is Robust to Heteroskedastic Noise. SIAM Journal On Mathematics Of Data Science 2021, 3: 388-413. PMID: 34124607, PMCID: PMC8194191, DOI: 10.1137/20m1342124.Peer-Reviewed Original ResearchStochastic normalizationHeteroskedastic noiseGaussian kernelHigh-dimensional settingsMatrix convergesAmbient dimensionDifferent noise variancesEuclidean spaceData pointsNoise varianceSymmetric normalizationCertain normalizationAffinity matrixClean counterpartsPairwise distancesKernelNoiseData analysis techniqueSingle-cell RNA-sequencing dataParticular directionSpaceWidespread approachConvergesMatrixHeteroskedasticity
2019
Direct List Mode Parametric Reconstruction for Dynamic Cardiac SPECT
Shi L, Lu Y, Wu J, Gallezot JD, Boutagy N, Thorn S, Sinusas AJ, Carson RE, Liu C. Direct List Mode Parametric Reconstruction for Dynamic Cardiac SPECT. IEEE Transactions On Medical Imaging 2019, 39: 119-128. PMID: 31180845, PMCID: PMC7030971, DOI: 10.1109/tmi.2019.2921969.Peer-Reviewed Original ResearchConceptsAppropriate kinetic modelConventional indirect methodImage reconstruction algorithmKinetic modelHigh noise levelsLow count levelsVivo canine studyIndirect methodImage noiseNoise levelParametric reconstructionNoiseReconstruction algorithmFrame imagePatient radiation dose reductionMethodDirect methodLower image noiseAutomatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis
Lee K, Khoo HM, Fourcade C, Gotman J, Grova C. Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis. Magnetic Resonance Imaging 2019, 58: 97-107. PMID: 30695721, DOI: 10.1016/j.mri.2019.01.019.Peer-Reviewed Original ResearchConceptsReal dataNumber of atomsSubject-specific thresholdFunctional connectivity MRI analysisState networksAutomatic removal methodSpatial priorsSet of voxelsBootstrap resamplingSparse dictionary learningStepwise regression procedureNoiseHub analysisRegression procedureInter-network communicationNew methodAtomsBand-pass filteringTemporal correlationFluctuationsPriorsSparsityDictionary learningNetworkWhole-brain signals
2018
Impact of measurement noise, experimental design, and estimation methods on Modular Response Analysis based network reconstruction
Thomaseth C, Fey D, Santra T, Rukhlenko OS, Radde NE, Kholodenko BN. Impact of measurement noise, experimental design, and estimation methods on Modular Response Analysis based network reconstruction. Scientific Reports 2018, 8: 16217. PMID: 30385767, PMCID: PMC6212399, DOI: 10.1038/s41598-018-34353-3.Peer-Reviewed Original ResearchConceptsModular Response AnalysisNetwork reconstructionSteady-state response curvesStatistical conceptsMeasurement noisePropagation of noiseNoise settingsEstimation methodNetwork structureTerms of accuracyLarge perturbationsResponse analysisDifferent replicatesPerturbation dataRegression strategyNoise
2015
Non-Local and Motion-Based Low-Rank Regularizations for Gated CT Reconstruction
Kim K, Fakhri G, Li Q. Non-Local and Motion-Based Low-Rank Regularizations for Gated CT Reconstruction. 2015, 1-3. DOI: 10.1109/nssmic.2015.7582219.Peer-Reviewed Original ResearchLow-rank regularizationNon-local weightsRegistration matrixLow-rank propertyMulti-frame imagesHigh noiseNon-local regularizationImage patchesMotion blurring artifactsConcurrent executionIterative reconstruction algorithmBlurring artifactsMotion-basedReconstruction algorithmMotion patternsNon-localReduce noiseImage qualityLow-dose conditionsComputer simulationsMotion artifactsNoiseGated computed tomographyGated CTRegularization
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