Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis
Sharifi G, Hajibeygi R, Zamani S, Easa A, Bahrami A, Eshraghi R, Moafi M, Ebrahimi M, Fathi M, Mirjafari A, Chan J, Dixe de Oliveira Santo I, Anar M, Rezaei O, Tu L. Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis. Emergency Radiology 2024, 32: 97-111. PMID: 39680295, DOI: 10.1007/s10140-024-02300-7.Peer-Reviewed Original ResearchConceptsConvolutional neural networkArea under the receiver operating characteristic curveConvolutional neural network modelCT scanSkull fractureComputed tomographyDeep learningProspective clinical trialMeta-analysisReceiver operating characteristic curvePublication biasSkull fracture detectionSystematic reviewNeural network algorithmDetecting skull fracturesImprove diagnosis accuracyDiagnostic hurdlesShortage of radiologistsAutomated diagnostic toolTransfer learningDiagnostic performanceDiagnostic accuracyClinical trialsModel architectureNeural networkBrain networks and intelligence: A graph neural network based approach to resting state fMRI data
Thapaliya B, Akbas E, Chen J, Sapkota R, Ray B, Suresh P, Calhoun V, Liu J. Brain networks and intelligence: A graph neural network based approach to resting state fMRI data. Medical Image Analysis 2024, 101: 103433. PMID: 39708510, PMCID: PMC11877132, DOI: 10.1016/j.media.2024.103433.Peer-Reviewed Original ResearchConceptsGraph neural networksNeural networkGraph isomorphism networkGraph convolutional layersGraph convolutional networkMachine learning modelsNetwork connectivity matrixCognitive processesConvolutional layersConvolutional networkPrediction taskModel architectureGraph architectureAdolescent Brain Cognitive Development datasetResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingLearning modelsMiddle frontal gyrusPredicting individual differencesResting state fMRI dataPredictive intelligenceIntelligenceNetworkFunctional network connectivity matricesArchitectureBrain networks and intelligence: A graph neural network based approach to resting state fMRI data
Thapaliya B, Akbas E, Chen J, Sapkota R, Ray B, Suresh P, Calhoun V, Liu J. Brain networks and intelligence: A graph neural network based approach to resting state fMRI data. Medical Image Analysis 2024, 101: 103433. PMID: 37986729, PMCID: PMC10659448, DOI: 10.1016/j.media.2024.103433.Peer-Reviewed Original ResearchGraph neural networksNeural networkGraph isomorphism networkGraph convolutional layersGraph convolutional networkMachine learning modelsMean square errorNetwork connectivity matrixCognitive processesConvolutional layersConvolutional networkPrediction taskModel architectureGraph architectureAdolescent Brain Cognitive Development datasetResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingLearning modelsMiddle frontal gyrusPredicting individual differencesResting state fMRI dataPredictive intelligenceIntelligenceNetworkFunctional network connectivity matricesL2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models
Ni A, Yin P, Zhao Y, Riddell M, Feng T, Shen R, Yin S, Liu Y, Yavuz S, Xiong C, Joty S, Zhou Y, Radev D, Cohan A, Cohan A. L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models. Transactions Of The Association For Computational Linguistics 2024, 12: 1311-1329. DOI: 10.1162/tacl_a_00705.Peer-Reviewed Original ResearchLanguage modelNatural language inputSemantic parsingHuman evaluationPretraining dataModel architectureModel sizeGeneration capabilityConfidence calibrationLearning paradigmPython programProject websiteTaskCapabilityLanguage inputParsingComprehensive evaluationLanguagePythonArchitectureCodeEvaluationLLMFramework1LearningIdentifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures
Ellis C, Sancho M, Miller R, Calhoun V. Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures. Communications In Computer And Information Science 2024, 2156: 102-124. DOI: 10.1007/978-3-031-63803-9_6.Peer-Reviewed Original ResearchDeep learning modelsExplainability methodsExplainability analysisConvolutional neural network architectureLearning modelsRaw electroencephalogramNeural network architectureDeep learning architectureMajor depressive disorderLearning architectureNetwork architectureDeep learningModel architectureMultichannel electroencephalogramTraining approachArchitectureBiomarkers of depressionFrequency bandElectroencephalogramResearch contextDepressive disorderElectroencephalogram biomarkerAccuracyRight hemisphereExplainabilityScepter: Weakly Supervised Framework for Spatiotemporal Dense Prediction of 4D Dynamic Brain Networks
Kazemivash B, Suresh P, Liu J, Ye D, Calhoun V. Scepter: Weakly Supervised Framework for Spatiotemporal Dense Prediction of 4D Dynamic Brain Networks. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039527, DOI: 10.1109/embc53108.2024.10781876.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingDynamic brain networksDense predictionBrain networksDynamic patterns of neural activityPatterns of neural activityBrain dynamicsSpatiotemporal brain dynamicsConsistent with previous findingsWeakly supervised frameworkComputer visionWeak supervisionModel architectureNetwork issuesSupervised frameworkFMRI dataBrain parcellation methodBrain functionNeural activityNeuroscience researchComplexity of brain functionNeural interactionsDeep-stackingExperimental resultsNetworkA survey of generative AI for de novo drug design: new frontiers in molecule and protein generation
Tang X, Dai H, Knight E, Wu F, Li Y, Li T, Gerstein M. A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation. Briefings In Bioinformatics 2024, 25: bbae338. PMID: 39007594, PMCID: PMC11247410, DOI: 10.1093/bib/bbae338.Peer-Reviewed Original Research
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