2022
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 Applications for Acute Stroke Management
Chavva IR, Crawford AL, Mazurek MH, Yuen MM, Prabhat AM, Payabvash S, Sze G, Falcone GJ, Matouk CC, de Havenon A, Kim JA, Sharma R, Schiff SJ, Rosen MS, Kalpathy‐Cramer J, Gonzalez J, Kimberly WT, Sheth KN. Deep Learning Applications for Acute Stroke Management. Annals Of Neurology 2022, 92: 574-587. PMID: 35689531, DOI: 10.1002/ana.26435.Peer-Reviewed Original ResearchConceptsDeep machine learningDeep learning applicationsMedical image analysisDeep neural networksPixel-wise labelingAcute stroke managementReal-world examplesDL applicationsDL approachMachine learningLearning applicationsDL modelsNeural networkStroke managementLesion segmentationMaximal utilityImage analysisElectronic medical record dataInter-rater variabilityCause of disabilityMedical record dataRelevant clinical featuresStroke detectionAdvanced neuroimaging techniquesDecision makingBrain Tumor Imaging: Applications of Artificial Intelligence
Afridi M, Jain A, Aboian M, Payabvash S. Brain Tumor Imaging: Applications of Artificial Intelligence. Seminars In Ultrasound CT And MRI 2022, 43: 153-169. PMID: 35339256, PMCID: PMC8961005, DOI: 10.1053/j.sult.2022.02.005.Peer-Reviewed Original ResearchConceptsArtificial intelligenceDeep learning systemDeep learning-based artificial intelligenceMachine learningImage processingLearning systemIntelligencePopular fieldDecision-making processPredictive modelRadiomic featuresNeuro-oncologyDecision-making protocolClinical decision-making protocolsMachineClinical decision-making processLearningBrain tumor imagingFeaturesClassificationImaging featuresProcessingTreatment responseMolecular classificationProtocol
2018
A user-guided tool for semi-automated cerebral microbleed detection and volume segmentation: Evaluating vascular injury and data labelling for machine learning
Morrison MA, Payabvash S, Chen Y, Avadiappan S, Shah M, Zou X, Hess CP, Lupo JM. A user-guided tool for semi-automated cerebral microbleed detection and volume segmentation: Evaluating vascular injury and data labelling for machine learning. NeuroImage Clinical 2018, 20: 498-505. PMID: 30140608, PMCID: PMC6104340, DOI: 10.1016/j.nicl.2018.08.002.Peer-Reviewed Original ResearchConceptsData labelingTraining dataHigh-level feature extractionVolume segmentationComputer-aided detection algorithmComputer-aided detection methodsGround truth labelingCerebral microbleed detectionFalse positivesMachine learningFeature extractionSegmentation resultsDetection algorithmSophisticated machineTime usersAlgorithm performanceCMB detectionComputer aidMicrobleed detectionSegmentationTest setDetection methodSuperior performanceExtensive research effortsMachine