Dataset on acute stroke risk stratification from CT angiographic radiomics
Avery EW, Behland J, Mak A, Haider SP, Zeevi T, Sanelli PC, Filippi CG, Malhotra A, Matouk CC, Griessenauer CJ, Zand R, Hendrix P, Abedi V, Falcone GJ, Petersen N, Sansing LH, Sheth KN, Payabvash S. Dataset on acute stroke risk stratification from CT angiographic radiomics. Data In Brief 2022, 44: 108542. PMID: 36060820, PMCID: PMC9428796, DOI: 10.1016/j.dib.2022.108542.Peer-Reviewed Original ResearchMachine Learning FrameworkImage processing technologyFeature selection algorithmField of radiomicsRadiomics-based analysisMachine learningMedical imagesSelection algorithmAssistance toolRadiomic featuresRadiomics dataProcessing technologyAnalysis frameworkRelevant informationRadiomics algorithmAlgorithmCT angiography imagesRadiomicsMethodological supportExternal testingFrameworkImagesAngiography imagesMachineFeaturesDeep learning for quality assessment of optical coherence tomography angiography images
Dhodapkar RM, Li E, Nwanyanwu K, Adelman R, Krishnaswamy S, Wang JC. Deep learning for quality assessment of optical coherence tomography angiography images. Scientific Reports 2022, 12: 13775. PMID: 35962007, PMCID: PMC9374672, DOI: 10.1038/s41598-022-17709-8.Peer-Reviewed Original ResearchConceptsImage identificationDeep learning-based systemLearning-based systemNeural network classifierLow-quality imagesSupervised learning modelNeural network modelImage qualityHigh-quality imagesMachine learningNetwork classifierLearning modelGround truthNetwork modelCurve metricsOptical coherence tomography angiography (OCTA) imagesImagesSignal strengthOptical coherence tomography angiographyTomography angiography imagesAngiography imagesQuality assessmentRobust methodImageNetClassifier
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