Monte-Carlo Frequency Dropout for Predictive Uncertainty Estimation in Deep Learning
Zeevi T, Venkataraman R, Staib L, Onofrey J. Monte-Carlo Frequency Dropout for Predictive Uncertainty Estimation in Deep Learning. 2024, 00: 1-5. DOI: 10.1109/isbi56570.2024.10635511.Peer-Reviewed Original ResearchArtificial neural networkState-of-the-artMedical image dataPredictive uncertainty estimationBiomedical image dataImage dataOptimal artificial neural networkMC dropoutDropout approachSource-codeDrop-connectDeep learningNeural networkSignal spaceMonte-CarloPrediction uncertaintyUncertainty estimationDiverse setComprehensive comparisonPrediction scenariosDeepPosterior predictive distributionRepositoryDecision-makingNetworkIn vivo neuropil density from anatomical MRI and machine learning
Akif A, Staib L, Herman P, Rothman D, Yu Y, Hyder F. In vivo neuropil density from anatomical MRI and machine learning. Cerebral Cortex 2024, 34: bhae200. PMID: 38771239, PMCID: PMC11107380, DOI: 10.1093/cercor/bhae200.Peer-Reviewed Original ResearchConceptsMagnetic resonance imagingSynaptic densityNeuropil densityCellular densityArtificial neural networkNeural networkPositron emission tomographyAnatomical magnetic resonance imagingHealthy subjectsSynaptic activityMRI scansMachine learning algorithmsBrain's energy budgetEmission tomographyIn vivo MRI scansResonance imagingTissue cellularityLearning algorithmsDiffusion magnetic resonance imagingMachine learningMicroscopic interpretationInterpretation of functional neuroimaging dataIndividual predictionsSubjects