2024
A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction
Fu S, Wang L, He H, Wen A, Zong N, Kumari A, Liu F, Zhou S, Zhang R, Li C, Wang Y, St Sauver J, Liu H, Sohn S. A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction. Journal Of The American Medical Informatics Association 2024, 31: 1493-1502. PMID: 38742455, PMCID: PMC11187420, DOI: 10.1093/jamia/ocae101.Peer-Reviewed Original ResearchNatural language processingClinical concept extractionElectronic health recordsConcept extractionClinical natural language processingConcept extraction taskNatural language processing modelsElectronic health record settingsDomain-specific knowledgeError taxonomyHeterogeneity of electronic health recordsReal-world dataAnalysis processExtraction taskOWL formatAnnotated examplesError analysisNLP modelsCommunity feedbackLanguage processingMulti-site settingConduct error analysisError classesError typesError analysis process
2018
Deep Learning Retinal Vessel Segmentation from a Single Annotated Example: An Application of Cyclic Generative Adversarial Neural Networks
Sadda P, Onofrey J, Papademetris X. Deep Learning Retinal Vessel Segmentation from a Single Annotated Example: An Application of Cyclic Generative Adversarial Neural Networks. Lecture Notes In Computer Science 2018, 11043: 82-91. DOI: 10.1007/978-3-030-01364-6_10.Peer-Reviewed Original ResearchGenerative adversarial neural networksAdversarial neural networkGround truth segmentationNeural networkTruth segmentationMedical image segmentation tasksImage segmentation tasksConvolutional neural networkDeep learning methodsRetinal vessel segmentationConvolutional networkSegmentation taskTraining examplesAnnotated examplesTraining dataLearning methodsVessel segmentationSegmentationSynthetic examplesNetworkLarge amountDatasetTaskExampleAccuracy
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