2024
Diffusion-based Bayesian posterior distribution prediction of kinetic parameters in dynamic PET
Djebra Y, Liu X, Marin T, Tiss A, Dhaynaut M, Guehl N, Johnson K, Fakhri G, Ma C, Ouyang J. Diffusion-based Bayesian posterior distribution prediction of kinetic parameters in dynamic PET. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657955.Peer-Reviewed Original ResearchConditional variational autoencoderEfficient deep learning-based approachMarkov chain Monte CarloDenoising diffusion probabilistic modelDeep learning-based approachDiffusion probabilistic modelLearning-based approachApproximate posterior distributionPosterior distributionVariational autoencoderHeavy computationTau protein aggregationBayesian inferenceProbabilistic modelData-drivenStudy molecular processesBayesian posterior distributionProtein aggregationMetropolis-Hastings Markov chain Monte CarloMolecular processesAlzheimer's diseaseNeurodegenerative diseasesKinetic parametersEstimate posterior distributionsAutoencoderAblation Study of Diffusion Model with Transformer Backbone for Low-count PET Denoising
Huang Y, Liu X, Miyazaki T, Omachi S, Fakhri G, Ouyang J. Ablation Study of Diffusion Model with Transformer Backbone for Low-count PET Denoising. 2011 IEEE Nuclear Science Symposium Conference Record 2024, 00: 1-2. PMID: 39445309, PMCID: PMC11497477, DOI: 10.1109/nss/mic/rtsd57108.2024.10655179.Peer-Reviewed Original ResearchIR tasksImage restorationImage super-resolution taskField of image restorationSuper-resolution taskLatent feature spaceConventional UNetDenoising iterationDenoising taskTransformer backboneDenoising autoencoderTexture restorationVision transformerFeature spaceAblation studiesLearning schemeBackbone networkImage generationDenoisingUNetIR modelPSNRSpatial informationAutoencoderTask
2018
Deep networks in identifying CT brain hemorrhage
Helwan A, El-Fakhri G, Sasani H, Uzun Ozsahin D. Deep networks in identifying CT brain hemorrhage. Journal Of Intelligent & Fuzzy Systems 2018, Preprint: 1-1. DOI: 10.3233/jifs-172261.Peer-Reviewed Original ResearchConvolutional neural networkStacked autoencoderDeep networksMedical image classificationDeep learning algorithmsMedical expert's experienceImage classificationTraining timeLearning algorithmsNeural networkAutoencoderExpert experienceBrain CT imagesCT imagesNetworkHigher accuracyLess errorAlgorithmImagesAccuracyErrorClassification