2023
Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning
Kucukkaya A, Zeevi T, Chai N, Raju R, Haider S, Elbanan M, Petukhova-Greenstein A, Lin M, Onofrey J, Nowak M, Cooper K, Thomas E, Santana J, Gebauer B, Mulligan D, Staib L, Batra R, Chapiro J. Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning. Scientific Reports 2023, 13: 7579. PMID: 37165035, PMCID: PMC10172370, DOI: 10.1038/s41598-023-34439-7.Peer-Reviewed Original Research
2022
Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis
Bahar RC, Merkaj S, Petersen G, Tillmanns N, Subramanian H, Brim WR, Zeevi T, Staib L, Kazarian E, Lin M, Bousabarah K, Huttner AJ, Pala A, Payabvash S, Ivanidze J, Cui J, Malhotra A, Aboian MS. Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis. Frontiers In Oncology 2022, 12: 856231. PMID: 35530302, PMCID: PMC9076130, DOI: 10.3389/fonc.2022.856231.Peer-Reviewed Original ResearchMachine learning modelsLearning modelConvolutional neural networkDeep learning studiesLarge training datasetsGrade predictionSupport vector machineApplication of MLNeural networkConventional machineVector machineTraining datasetBest performing modelCommon algorithmsModel performanceEssential metricMean prediction accuracyHigh predictive accuracyPrediction accuracyPerforming modelMachinePrediction modelDiagnosis statementsAccuracy statementsLearning studiesMachine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment
Petersen G, Shatalov J, Verma T, Brim WR, Subramanian H, Brackett A, Bahar RC, Merkaj S, Zeevi T, Staib LH, Cui J, Omuro A, Bronen RA, Malhotra A, Aboian MS. Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment. American Journal Of Neuroradiology 2022, 43: 526-533. PMID: 35361577, PMCID: PMC8993193, DOI: 10.3174/ajnr.a7473.Peer-Reviewed Original ResearchConceptsMachine learning-based methodsLearning-based methodsBalanced data setData setsVector machine modelMachine learningClassification algorithmsMachine modelMachineAlgorithmData basesPrediction modelPromising resultsPrimary CNS lymphomaPrediction model study RiskRisk of biasRadiomic featuresClassifierSetCNS lymphomaWebLearningFeaturesQualitySystematic reviewIdentifying 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
2020
Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation
Onofrey JA, Staib LH, Huang X, Zhang F, Papademetris X, Metaxas D, Rueckert D, Duncan JS. Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation. Annual Review Of Biomedical Engineering 2020, 22: 1-27. PMID: 32169002, PMCID: PMC9351438, DOI: 10.1146/annurev-bioeng-060418-052147.Peer-Reviewed Original Research
2018
2-Channel Convolutional 3D Deep Neural Network (2CC3D) for FMRI Analysis: ASD Classification and Feature Learning
Li X, Dvornek NC, Papademetris X, Zhuang J, Staib LH, Ventola P, Duncan JS. 2-Channel Convolutional 3D Deep Neural Network (2CC3D) for FMRI Analysis: ASD Classification and Feature Learning. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2018, 2018: 1252-1255. PMID: 32983370, PMCID: PMC7519578, DOI: 10.1109/isbi.2018.8363798.Peer-Reviewed Original ResearchConvolutional neural networkNeural networkCNN convolutional layerSpatial featuresASD classificationDeep neural networksMean F-scoreTraditional machineFeature learningConvolutional layersInput formatF-scoreClassification modelTemporal informationNetworkWindow parametersImagesClassificationConvolutionalTemporal statisticsMachineLearningFeaturesFormatScheme