2025
Improving Quality Control of MRI Images Using Synthetic Motion Data
Bricout C, Cho K, Harms M, Pasternak O, Bearden C, McGorry P, Kahn R, Kane J, Nelson B, Woods S, Shenton M, Bouix S, Kahou S. Improving Quality Control of MRI Images Using Synthetic Motion Data. 2025, 00: 1-4. DOI: 10.1109/isbi60581.2025.10981056.Peer-Reviewed Original Research
2024
CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning
Du Y, Chang B, Dvornek N. CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning. Lecture Notes In Computer Science 2024, 15012: 465-475. PMID: 39791126, PMCID: PMC11709740, DOI: 10.1007/978-3-031-72390-2_44.Peer-Reviewed Original ResearchContrastive Language-Image Pre-trainingLanguage modelState-of-the-art performanceSelf-supervised representation learningContrastive learning methodFine-tuningProlonged training timeBERT encoderContrastive learningRepresentation learningClass labelsGPU resourcesTraining samplesTraining timeMammography datasetModel sizePre-trainingLearning methodsEfficient frameworkVisual modelRichness of informationDatasetClinical diagnostic dataLearningMedical applications
2022
Tracing and Forecasting Metabolic Indices of Cancer Patients Using Patient-Specific Deep Learning Models
Hou J, Deng J, Li C, Wang Q. Tracing and Forecasting Metabolic Indices of Cancer Patients Using Patient-Specific Deep Learning Models. Journal Of Personalized Medicine 2022, 12: 742. PMID: 35629164, PMCID: PMC9147215, DOI: 10.3390/jpm12050742.Peer-Reviewed Original ResearchShort-term memory recurrent neural networkLong short-term memory recurrent neural networkTransfer learningRecurrent neural networkDeep learning modelsReduced training timeNeural networkLearning modelTraining timeDynamical system modelLearningShort-term predictionNormsMore cancer patientsComparable levelsPhysiological modelPatient-specific models
2020
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE.
Zhuang J, Dvornek N, Li X, Tatikonda S, Papademetris X, Duncan J. Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE. Proceedings Of Machine Learning Research 2020, 119: 11639-11649. PMID: 34308361, PMCID: PMC8299461.Peer-Reviewed Original ResearchNeural ordinary differential equationsComputation graphImage classification tasksClassification taskPyTorch implementationBenchmark tasksTraining timeAdaptive checkpointsNeural ODEAutomatic differentiationNaive methodTime series modelingRedundant componentsGradient estimation methodError rateGood accuracyPhysical knowledgeEmpirical performanceGraphGradient estimationTaskAccuracyODE solverSolverResNet
2018
Deep networks in identifying CT brain hemorrhage
Helwan A, El-Fakhri G, Sasani H, Uzun Ozsahin D. Deep networks in identifying CT brain hemorrhage. Journal Of Intelligent & Fuzzy Systems 2018, Preprint: 1-1. DOI: 10.3233/jifs-172261.Peer-Reviewed Original ResearchConvolutional neural networkStacked AutoencoderDeep networksMedical image classificationDeep learning algorithmsMedical expert's experienceImage classificationTraining timeLearning algorithmsNeural networkAutoencoderExpert experienceBrain CT imagesCT imagesNetworkHigher accuracyLess errorAlgorithmImagesAccuracyErrorClassification
2016
Standardizing Care and Parental Training to Improve Training Duration, Referral Frequency, and Length of Stay: Our Quality Improvement Project Experience
Tolomeo C, Major NE, Szondy MV, Bazzy-Asaad A. Standardizing Care and Parental Training to Improve Training Duration, Referral Frequency, and Length of Stay: Our Quality Improvement Project Experience. Journal Of Pediatric Nursing 2016, 32: 72-79. PMID: 28341025, DOI: 10.1016/j.pedn.2016.10.004.Peer-Reviewed Original ResearchConceptsTechnology-dependent infantsRespiratory care unitLength of stayTracheostomy tubeCare unitParents of infantsParental trainingPatient careDependent infantsDevelopmental interventionsTraining leadParent/guardian educationDevelopmental assessmentProficiency trainingQuality improvement projectTraining durationSustained improvementQI approachReferral frequencyInfantsTraining timeConvenience sampleParentsCareQI project
2012
Clinical entity recognition using structural support vector machines with rich features
Tang B, Cao H, Wu Y, Jiang M, Xu H. Clinical entity recognition using structural support vector machines with rich features. 2012, 13-20. DOI: 10.1145/2390068.2390073.Peer-Reviewed Original ResearchStructural support vector machineClinical entity recognitionSupport vector machineConditional Random FieldsNatural language processingEntity recognitionVector machineRich featuresNLP challengeSequential labeling algorithmLarge margin theoryUnsupervised word representationsClinical text processingConcept extraction taskLess training timeHighest F-measureTest setI2b2 NLP challengeExtraction taskTypical machineNER taskClinical textTraining timeF-measureLanguage processing
1995
Learning rare categories in backpropagation
Ohno-Machado L, Musen M. Learning rare categories in backpropagation. Lecture Notes In Computer Science 1995, 991: 201-209. DOI: 10.1007/bfb0034813.Peer-Reviewed Original Research
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