2023
Learning Sequential Information in Task-Based fMRI for Synthetic Data Augmentation
Wang J, Dvornek N, Staib L, Duncan J. Learning Sequential Information in Task-Based fMRI for Synthetic Data Augmentation. Lecture Notes In Computer Science 2023, 14312: 79-88. PMID: 39281201, PMCID: PMC11395879, DOI: 10.1007/978-3-031-44858-4_8.Peer-Reviewed Original ResearchFunctional magnetic resonance imagesData augmentationClassification taskSpecific cognitive tasksMedical image analysisSynthetic data augmentationEffective data augmentationDownstream learning tasksCognitive tasksVariational autoencoder modelLearning taskTraining dataAutoencoder modelTemporal informationTraining datasetSequential informationSynthetic imagesTaskFMRI sequencesImage analysisMultiple perspectivesMagnetic resonance imagesImagesDifferent alternativesPersistent issue
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 studies