2022
Identifying 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
2015
How I Do It A Practical Database Management System to Assist Clinical Research Teams with Data Collection, Organization, and Reporting
Lee H, Chapiro J, Schernthaner R, Duran R, Wang Z, Gorodetski B, Geschwind JF, Lin M. How I Do It A Practical Database Management System to Assist Clinical Research Teams with Data Collection, Organization, and Reporting. Academic Radiology 2015, 22: 527-533. PMID: 25641319, PMCID: PMC4355209, DOI: 10.1016/j.acra.2014.12.002.Peer-Reviewed Original ResearchConceptsDatabase management systemDatabase systemsManagement systemUser interface systemData storage problemCustom searchRetrieval criteriaInterface systemData inputAutomatic calculationStorage problemData collectionStorage systemDatabaseRetrievingStoringSystemWorkflowRobust toolBoard-certified radiologists