2024
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-makingNetworkTAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction
Guo X, Shi L, Chen X, Liu Q, Zhou B, Xie H, Liu Y, Palyo R, Miller E, Sinusas A, Staib L, Spottiswoode B, Liu C, Dvornek N. TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction. Medical Image Analysis 2024, 96: 103190. PMID: 38820677, PMCID: PMC11180595, DOI: 10.1016/j.media.2024.103190.Peer-Reviewed Original ResearchGenerative adversarial networkAdversarial networkMotion estimation accuracyInter-frame motionIntensity-based image registration techniqueAll-to-oneSegmentation masksImage registration techniquesOriginal frameTemporal informationDiagnosis accuracyMyocardial blood flowEstimation accuracyFrame conversionPositron emission tomographyNovel methodImage qualityPET datasetsRegistration techniqueNetworkCardiac positron emission tomographyBlood flowDynamic cardiac positron emission tomographyMotion correctionCoronary artery diseaseMulti-Task Learning for Motion Analysis and Segmentation in 3D Echocardiography
Ta K, Ahn S, Thorn S, Stendahl J, Zhang X, Langdon J, Staib L, Sinusas A, Duncan J. Multi-Task Learning for Motion Analysis and Segmentation in 3D Echocardiography. IEEE Transactions On Medical Imaging 2024, 43: 2010-2020. PMID: 38231820, PMCID: PMC11514714, DOI: 10.1109/tmi.2024.3355383.Peer-Reviewed Original ResearchMulti-task learning networkCross-stitch unitsComposite loss functionAccurate motion estimationTask-specific networksMotion estimationSegmentation masksLearning networkLoss functionSegmentation stepEchocardiography datasetNetworkMotion displacementMotion analysisMultiple time framesTaskAnalysis pipelineSegmentsStrain measurementsDatasetRepresentationChapter 13 Data-driven learning strategies for biomarker detection and outcome prediction in Autism from task-based fMRI
Duncan J, Staib L, Dvornek N, Li X, Zhuang J, Wang J, Ventola P. Chapter 13 Data-driven learning strategies for biomarker detection and outcome prediction in Autism from task-based fMRI. 2024, 357-393. DOI: 10.1016/b978-0-32-385124-4.00024-6.Peer-Reviewed Original ResearchLong short-term memoryGraph neural networksFunctional magnetic resonance imagingAutism spectrum disorderNeural ordinary differential equationsData-driven learning strategyDeep learning techniquesTask-based functional magnetic resonance imagingShort-term memoryNeural networkLearning techniquesImpaired social interactionTerm memoryBehavioral therapyRepetitive behaviorsSpectrum disorderDevelopmental disordersLearning strategiesSpatio-temporal characteristicsInherent dynamicsCharacterization of individualsModel of causalitySocial interactionNetworkPersonalized outcome predictions
2023
Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning
Kucukkaya A, Zeevi T, Chai N, Raju R, Haider S, Elbanan M, Petukhova-Greenstein A, Lin M, Onofrey J, Nowak M, Cooper K, Thomas E, Santana J, Gebauer B, Mulligan D, Staib L, Batra R, Chapiro J. Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning. Scientific Reports 2023, 13: 7579. PMID: 37165035, PMCID: PMC10172370, DOI: 10.1038/s41598-023-34439-7.Peer-Reviewed Original Research
2019
Generalizable Multi-Site Training and Testing Of Deep Neural Networks Using Image Normalization
Onofrey JA, Casetti-Dinescu DI, Lauritzen AD, Sarkar S, Venkataraman R, Fan RE, Sonn GA, Sprenkle PC, Staib LH, Papademetris X. Generalizable Multi-Site Training and Testing Of Deep Neural Networks Using Image Normalization. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2019, 00: 348-351. PMID: 32874427, PMCID: PMC7457546, DOI: 10.1109/isbi.2019.8759295.Peer-Reviewed Original ResearchDeep neural networksNeural networkDeep learning algorithmsProstate gland segmentationImage normalization methodGland segmentationLearning algorithmImage normalizationMulti-site dataIntensity normalization methodNormalization methodSingle-site dataAlgorithmNetworkPotential solutionsEquipment sourcesClinical adoptionSegmentationTrainingIntensity characteristicsRobustnessDataSite trainingMethodAdoption
2018
2-Channel Convolutional 3D Deep Neural Network (2CC3D) for FMRI Analysis: ASD Classification and Feature Learning
Li X, Dvornek NC, Papademetris X, Zhuang J, Staib LH, Ventola P, Duncan JS. 2-Channel Convolutional 3D Deep Neural Network (2CC3D) for FMRI Analysis: ASD Classification and Feature Learning. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2018, 2018: 1252-1255. PMID: 32983370, PMCID: PMC7519578, DOI: 10.1109/isbi.2018.8363798.Peer-Reviewed Original ResearchConvolutional neural networkNeural networkCNN convolutional layerSpatial featuresASD classificationDeep neural networksMean F-scoreTraditional machineFeature learningConvolutional layersInput formatF-scoreClassification modelTemporal informationNetworkWindow parametersImagesClassificationConvolutionalTemporal statisticsMachineLearningFeaturesFormatScheme