2024
Self-supervised Pre-training Tasks for an fMRI Time-Series Transformer in Autism Detection
Zhou Y, Duan P, Du Y, Dvornek N. Self-supervised Pre-training Tasks for an fMRI Time-Series Transformer in Autism Detection. Lecture Notes In Computer Science 2024, 15266: 145-154. DOI: 10.1007/978-3-031-78761-4_14.Peer-Reviewed Original ResearchSelf-supervised pre-training tasksPre-training tasksFunctional magnetic resonance imagingPre-training stepTransformer-based modelsTime-series fMRI dataTraining data availabilityAutism spectrum disorderTime series transformationsTransformation modelMachine learning methodsFunctional magnetic resonance imaging time-series dataClassification taskPublic datasetsTraining dataOver-fittingComputed functional connectivityLearning methodsModel performanceMasking strategyAutism detectionCross-validationTaskDatasetAverage improvement
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 issueChapter 13 Deep learning with connectomes
Dvornek N, Li X. Chapter 13 Deep learning with connectomes. 2023, 289-308. DOI: 10.1016/b978-0-323-85280-7.00013-0.ChaptersDeep learning modelsLearning modelDeep learningClassic computer visionNeural network architectureImage analysis problemsMachine learning methodsNeural network modelComputer visionPotential future workNetwork architectureNonlinear neural network modelArt resultsPrediction taskLearning methodsNetwork modelAnalysis problemUseful representationConnectomePopular typeLearningFuture workData analysisArchitectureTask
2018
Combining Phenotypic and Resting-State FMRI Data for Autism Classification with Recurrent Neural Networks
Dvornek NC, Ventola P, Duncan JS. Combining Phenotypic and Resting-State FMRI Data for Autism Classification with Recurrent Neural Networks. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2018, 2018: 725-728. PMID: 30288208, PMCID: PMC6166875, DOI: 10.1109/isbi.2018.8363676.Peer-Reviewed Original ResearchAutism spectrum disorderRecurrent neural networkNeural networkAutism Brain Imaging Data ExchangeSingle deep learning frameworkHeterogeneity of ASDFunctional magnetic resonance imagingDeep learning frameworkResting-state fMRI dataResting-state functional magnetic resonance imagingBetter classification accuracyAutism classificationSpectrum disorderData exchangeLearning frameworkFMRI dataClassification accuracyCross-validation frameworkChallenging taskStraightforward taskPrior workNetworkSuch dataRsfMRITask