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
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-71. IDENTIFYING CLINICALLY APPLICABLE MACHINE LEARNING ALGORITHMS FOR GLIOMA SEGMENTATION USING A SYSTEMATIC LITERATURE REVIEW
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-71. IDENTIFYING CLINICALLY APPLICABLE MACHINE LEARNING ALGORITHMS FOR GLIOMA SEGMENTATION USING A SYSTEMATIC LITERATURE REVIEW. Neuro-Oncology 2021, 23: vi145-vi145. PMCID: PMC8598815, DOI: 10.1093/neuonc/noab196.568.Peer-Reviewed Original ResearchConvolutional neural networkSegmentation of gliomasSupport vector machineGlioma segmentationDeep learningMachine learningLikelihood of overfittingMachine Learning AlgorithmsArtificial intelligenceLearning algorithmDice scoreML algorithmsTumor segmentationNeural networkVector machineCommon algorithmsSegmentationSame datasetML methodsTCIA datasetAlgorithmData acquisitionAccuracy reportingHigh accuracyLearning