2021
BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis
Li X, Zhou Y, Dvornek N, Zhang M, Gao S, Zhuang J, Scheinost D, Staib LH, Ventola P, Duncan JS. BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis. Medical Image Analysis 2021, 74: 102233. PMID: 34655865, PMCID: PMC9916535, DOI: 10.1016/j.media.2021.102233.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagesGraph neural network frameworkMedical image analysisGraph neural networkGraph convolutional layersNeural network frameworkDifferent evaluation metricsSpecific task statesIndependent fMRI datasetsPooling layerConvolutional layersConsistency lossNetwork frameworkNeural networkFMRI datasetsImage analysis methodEvaluation metricsDetection resultsBrain graphsSubjects releaseROI selectionImage analysisCognitive stimuliTask statesFMRI analysis
2020
Classification of Heart Sounds Using Convolutional Neural Network
Li F, Tang H, Shang S, Mathiak K, Cong F. Classification of Heart Sounds Using Convolutional Neural Network. Applied Sciences 2020, 10: 3956. DOI: 10.3390/app10113956.Peer-Reviewed Original ResearchConvolutional neural networkSignal-to-noise ratioNeural networkAlgorithm performanceClassification of heart soundGlobal average pooling layerAverage pooling layerStratified five-fold cross-validationLow signal-to-noise ratioDeep learning algorithmsClassification algorithms performanceClassification layerFeature mapsPooling layerDiagnosis of cardiac conditionsClass imbalanceFive-fold cross-validationLearning algorithmsGlobal informationMatthews correlation coefficientLoss functionClass weightsTraining processPhysioNet/CinC ChallengeClassification
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