2022
Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities
Merkaj S, Bahar RC, Zeevi T, Lin M, Ikuta I, Bousabarah K, Petersen G, Staib L, Payabvash S, Mongan JT, Cha S, Aboian MS. Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities. Cancers 2022, 14: 2623. PMID: 35681603, PMCID: PMC9179416, DOI: 10.3390/cancers14112623.Peer-Reviewed Original ResearchMachine learning toolsGrade predictionLearning toolsML applicationsClassifier algorithmML modelsClassification methodMedical imagingData sourcesPractices of radiologistsToolGlioma gradingNext stepWorkflowAlgorithmChallengesTechnological innovationImplementationPredictionModelLast decadeSpecific areas
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 model