2025
Towards Zero-Shot Task-Generalizable Learning on FMRI
Wang J, Dvornek N, Duan P, Staib L, Duncan J. Towards Zero-Shot Task-Generalizable Learning on FMRI. 2025, 00: 1-5. DOI: 10.1109/isbi60581.2025.10981094.Peer-Reviewed Original ResearchContextual informationLearn contextual informationNeural network architectureTask-based fMRIPlug-and-playDownstream tasksNetwork architectureTask-dependent signalsTask-based paradigmsFunctional brain patternsResting-state fMRIMultiple modulesTaskFMRI taskBrain activityArchitectureBOLD signalResting-stateFMRIBrain functionBrain patternsTask designResting stateInformationEncoding
2024
Spatiotemporal Deep Learning-Based Cine Loop Quality Filter for Handheld Point-of-Care Echocardiography
Mukaddim R, Mackay E, Gessert N, Erkamp R, Sethuraman S, Sutton J, Bharat S, Jutras M, Baloescu C, Moore C, Raju B. Spatiotemporal Deep Learning-Based Cine Loop Quality Filter for Handheld Point-of-Care Echocardiography. IEEE Transactions On Ultrasonics Ferroelectrics And Frequency Control 2024, 71: 1577-1587. PMID: 38700961, DOI: 10.1109/tuffc.2024.3396796.Peer-Reviewed Original ResearchOptical flow framesHigh-quality framesLow-quality framesNeural network architectureDeep learning modelsInput framesFrame levelEcho framesNetwork architectureSpatiotemporal deep learning modelCNN modelTemporal informationLV borderLearning modelsTest datasetSpatial informationFlow frameCNNImage qualityPoint-of-careQuantification algorithmHandheldAutomated quantification algorithmImage artifactsImage interpretationIdentifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures
Ellis C, Sancho M, Miller R, Calhoun V. Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures. Communications In Computer And Information Science 2024, 2156: 102-124. DOI: 10.1007/978-3-031-63803-9_6.Peer-Reviewed Original ResearchDeep learning modelsExplainability methodsExplainability analysisConvolutional neural network architectureLearning modelsRaw electroencephalogramNeural network architectureDeep learning architectureMajor depressive disorderLearning architectureNetwork architectureDeep learningModel architectureMultichannel electroencephalogramTraining approachArchitectureBiomarkers of depressionFrequency bandElectroencephalogramResearch contextDepressive disorderElectroencephalogram biomarkerAccuracyRight hemisphereExplainability
2023
Chapter 13 Deep learning with connectomes
Dvornek N, Li X. Chapter 13 Deep learning with connectomes. 2023, 289-308. DOI: 10.1016/b978-0-323-85280-7.00013-0.ChaptersDeep learning modelsLearning modelDeep learningClassic computer visionNeural network architectureImage analysis problemsMachine learning methodsNeural network modelComputer visionPotential future workNetwork architectureNonlinear neural network modelArt resultsPrediction taskLearning methodsNetwork modelAnalysis problemUseful representationConnectomePopular typeLearningFuture workData analysisArchitectureTask
2021
Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients
Deng Y, Lu L, Aponte L, Angelidi A, Novak V, Karniadakis G, Mantzoros C. Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients. Npj Digital Medicine 2021, 4: 109. PMID: 34262114, PMCID: PMC8280162, DOI: 10.1038/s41746-021-00480-x.Peer-Reviewed Original ResearchData augmentationTransfer learningNetwork architectureData augmentation techniquesNeural network architectureTransfer-learning strategyDeep-learning methodsDeep learning modelsDeep transfer learningTransfer-learning methodsSame network architectureDifferent loss functionsFuture glucose levelsAccurate predictive modelsH prediction horizonPublic datasetsGenerative modelGlucose level predictionAugmentation techniquesLoss functionPrediction accuracyDatasetLevel predictionPrediction horizonArchitecture
2019
Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death
Kulkarni PM, Robinson EJ, Pradhan J, Gartrell-Corrado RD, Rohr BR, Trager MH, Geskin LJ, Kluger HM, Wong PF, Acs B, Rizk EM, Yang C, Mondal M, Moore MR, Osman I, Phelps R, Horst BA, Chen ZS, Ferringer T, Rimm DL, Wang J, Saenger YM. Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death. Clinical Cancer Research 2019, 26: 1126-1134. PMID: 31636101, PMCID: PMC8142811, DOI: 10.1158/1078-0432.ccr-19-1495.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overAlgorithmsArea Under CurveBiopsyDeep LearningDisease ProgressionFemaleFollow-Up StudiesHumansImage Processing, Computer-AssistedMaleMelanomaMiddle AgedNeoplasm Recurrence, LocalNeural Networks, ComputerRetrospective StudiesRisk FactorsStaining and LabelingSurvival RateYoung AdultConceptsDeep neural network architectureNeural network architectureDeep learningNetwork architectureComputational modelImage sequencesDigital imagesVote aggregationDisease-specific survivalDSS predictionPractical advancesComputational methodsIHC-based methodsImagesGeisinger Health SystemNovel methodGHS patientsArchitectureLearningKaplan-Meier analysisPrimary melanoma tumorsEarly-stage melanomaClinical trial designModelAdjuvant immunotherapyIntegrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text
Li Z, Yang Z, Shen C, Xu J, Zhang Y, Xu H. Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text. BMC Medical Informatics And Decision Making 2019, 19: 22. PMID: 30700301, PMCID: PMC6354333, DOI: 10.1186/s12911-019-0736-9.Peer-Reviewed Original ResearchConceptsShortest dependency pathConvolutional neural networkNeural network architectureNatural language processingSentence sequenceRelation extractionClinical relation extractionTarget entityNetwork architectureClinical textNeural networkRepresentation moduleDependency pathsDeep learning-based approachNew neural network architectureBidirectional long short-term memory networkLong short-term memory networkDeep learning frameworkDeep neural networksShort-term memory networkLearning-based approachNovel neural approachRelation extraction datasetBi-LSTM networkSyntactic features
2018
Clinical Named Entity Recognition Using Deep Learning Models.
Wu Y, Jiang M, Xu J, Zhi D, Xu H. Clinical Named Entity Recognition Using Deep Learning Models. AMIA Annual Symposium Proceedings 2018, 2017: 1812-1819. PMID: 29854252, PMCID: PMC5977567.Peer-Reviewed Original ResearchConceptsClinical Named Entity RecognitionNamed Entity RecognitionDeep learning modelsConvolutional neural networkClinical NER systemRecurrent neural networkNeural networkLearning modelEntity recognitionRNN modelNER systemDeep neural network architecturePopular deep learning architecturesNatural language processing tasksUnsupervised learning featuresConditional random field modelAutomatic feature learningDeep learning architectureClinical NER tasksDeep neural networksNeural network architectureClinical concept extractionLanguage processing tasksFeature learningLearning architecture
2017
CNN-based ranking for biomedical entity normalization
Li H, Chen Q, Tang B, Wang X, Xu H, Wang B, Huang D. CNN-based ranking for biomedical entity normalization. BMC Bioinformatics 2017, 18: 385. PMID: 28984180, PMCID: PMC5629610, DOI: 10.1186/s12859-017-1805-7.Peer-Reviewed Original ResearchConceptsBiomedical entity normalizationEntity normalizationSemantic informationCNN architectureNovel convolutional neural network architectureConvolutional neural network architectureTraditional rule-based methodsNeural network architectureRule-based systemRanking methodRule-based methodNetwork architectureBiomedical entitiesBenchmark datasetsArt performanceEntity mentionsRanking problemCNNNormalization systemArchitectureMorphological informationComparison resultsInformationDatasetSystemA Neural Attention Model for Categorizing Patient Safety Events
Cohan A, Fong A, Goharian N, Ratwani R. A Neural Attention Model for Categorizing Patient Safety Events. Lecture Notes In Computer Science 2017, 10193: 720-726. DOI: 10.1007/978-3-319-56608-5_71.Peer-Reviewed Original ResearchNeural attention modelAttention modelPatient safety event reportingSafety eventsPatient safety eventsPatient safety reportsSafety event reportingPrevent medication errorsNeural network architectureMedication errorsNetwork architecturePotential adverse eventsEvent reportingLong sequencesSafety reportsEmpirical resultsAdverse eventsNeuralDatasetArchitecturePatientsMethodReportsCategorizationModel
1997
Sequential versus standard neural networks for pattern recognition: An example using the domain of coronary heart disease
Ohno-Machado L, Musen M. Sequential versus standard neural networks for pattern recognition: An example using the domain of coronary heart disease. Computers In Biology And Medicine 1997, 27: 267-281. PMID: 9303265, DOI: 10.1016/s0010-4825(97)00008-5.Peer-Reviewed Original ResearchMeSH KeywordsAdultAge FactorsAlgorithmsArea Under CurveBlood PressureBody WeightCause of DeathCholesterolCoronary DiseaseDatabases as TopicDemographyDisease ProgressionDisease-Free SurvivalEvaluation Studies as TopicFollow-Up StudiesForecastingHumansMaleMiddle AgedModels, CardiovascularNeural Networks, ComputerOutcome Assessment, Health CarePattern Recognition, AutomatedPrognosisROC CurveSmokingSurvival AnalysisTime FactorsConceptsNeural network modelNeural networkSequential neural network modelsTime-oriented dataNetwork modelNeural network architectureStandard neural networkSequential neural networkNeural network systemRecognition of patternsNetwork architecturePattern recognitionUnseen casesNetwork systemTest setSingle pointResearch data basesData basesNetworkMedical researchersSuch modelsRecognitionBackpropagationSetArchitecture
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