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 imagesMachineFeaturesIdentifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries
Tillmanns N, Lum AE, Cassinelli G, Merkaj S, Verma T, Zeevi T, Staib L, Subramanian H, Bahar RC, Brim W, Lost J, Jekel L, Brackett A, Payabvash S, Ikuta I, Lin M, Bousabarah K, Johnson MH, Cui J, Malhotra A, Omuro A, Turowski B, Aboian MS. Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries. Neuro-Oncology Advances 2022, 4: vdac093. PMID: 36071926, PMCID: PMC9446682, DOI: 10.1093/noajnl/vdac093.Peer-Reviewed Original ResearchGlioma segmentationResearch algorithmSegmentation of gliomasHigh accuracy resultsML algorithmsApplicable machineAccuracy resultsTCIA datasetSegmentationAlgorithmMachinePatient dataSystematic literature reviewOverfittingData extractionDatasetBratDatabaseRecent advancesResearch literatureLimitationsExtractionCurrent research literatureMethod
2021
NIMG-23. MACHINE LEARNING METHODS IN GLIOMA GRADE PREDICTION: A SYSTEMATIC REVIEW
Bahar R, Merkaj S, Brim W, Subramanian H, Zeevi T, Kazarian E, Lin M, Bousabarah K, Payabvash S, Ivanidze J, Cui J, Tocino I, Malhotra A, Aboian M. NIMG-23. MACHINE LEARNING METHODS IN GLIOMA GRADE PREDICTION: A SYSTEMATIC REVIEW. Neuro-Oncology 2021, 23: vi133-vi133. PMCID: PMC8598529, DOI: 10.1093/neuonc/noab196.523.Peer-Reviewed Original ResearchClassical machine learningConvolutional neural networkDeep learningSupport vector machineMachine learningMachine learning technologiesHigher grading accuracyMachine learning methodsArtificial intelligenceML applicationsHighest performing modelLearning technologyNeural networkMultimodal sequencesLearning methodsVector machineCommon algorithmsML methodsTCIA datasetPrimary machinePrediction accuracyGrade predictionGrading accuracyMachinePerforming modelNIMG-38. MEASURING ADHERENCE TO TRIPOD OF ARTIFICIAL INTELLIGENCE PAPERS IN THE GLIOMA SEGMENTATION
Tillmanns N, Lum A, Brim W, Subramanian H, Lin M, Bousabarah K, Malhotra A, cui J, Brackett A, Payabvash S, Ikuta I, Johnson M, Turowski B, Aboian M. NIMG-38. MEASURING ADHERENCE TO TRIPOD OF ARTIFICIAL INTELLIGENCE PAPERS IN THE GLIOMA SEGMENTATION. Neuro-Oncology 2021, 23: vi137-vi137. PMCID: PMC8598634, DOI: 10.1093/neuonc/noab196.537.Peer-Reviewed Original ResearchArtificial intelligence papersDeep learningArtificial intelligenceGlioma segmentationMachine learningModel performanceSegmentationNetwork descriptionMachineInclusion of informationPrediction modelLearningCritical elementsIntelligenceWebPerformanceScoring itemsKeywordsTRIPOD itemsRadiomicsItemsDatabaseInformationVocabularySearch