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
2000
PathMaster
Mattie M, Staib L, Stratmann E, Tagare H, Duncan J, Miller P. PathMaster. Journal Of The American Medical Informatics Association 2000, 7: 404-415. PMID: 10887168, PMCID: PMC61444, DOI: 10.1136/jamia.2000.0070404.Peer-Reviewed Original ResearchConceptsDigital image databaseText-based descriptionsFeature extraction routineImage databaseIndexing methodSearch enginesFeature extractionCytopathology imagesCross-reference analysisExtraction routinesImagesPrognostic processInformation contentDescriptorsIndividual cell characteristicsDatabaseCell descriptorsRoutinesRecognition trialsEngineIndex dataExtractionMethodDescription