2023
Image Intensity Normalization Benefits Deep Learning Brain PET Motion Correction
Lieffrig E, Zhang J, Zeng T, Cai Z, You C, Lu Y, Onofrey J. Image Intensity Normalization Benefits Deep Learning Brain PET Motion Correction. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338194.Peer-Reviewed Original ResearchInput data normalizationImage intensity normalizationNeural network inputsMedical imaging researchPET motion correctionPre-processing stepMotion prediction errorMotion correctionIntensity normalizationNetwork inputsMotion predictionHead motion correctionInput dataTesting subjectsData normalizationEarly framesSuch methodsPrediction errorImaging researchDifferent normalization strategiesNormalization strategyMachineAlgorithmTaskValue analysisPredicting 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
2020
Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation
Onofrey JA, Staib LH, Huang X, Zhang F, Papademetris X, Metaxas D, Rueckert D, Duncan JS. Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation. Annual Review Of Biomedical Engineering 2020, 22: 1-27. PMID: 32169002, PMCID: PMC9351438, DOI: 10.1146/annurev-bioeng-060418-052147.Peer-Reviewed Original ResearchSupervised Machine Learning in Oncology: A Clinician's Guide
Murali N, Kucukkaya A, Petukhova A, Onofrey J, Chapiro J. Supervised Machine Learning in Oncology: A Clinician's Guide. Digestive Disease Interventions 2020, 04: 073-081. PMID: 32869010, PMCID: PMC7456427, DOI: 10.1055/s-0040-1705097.Peer-Reviewed Original ResearchMachine learningSupervised machineSupervised machine learning methodsNew data processing technologiesLarge volume dataData processing technologySupervised machine learningMachine learning methodsSelf-improving modelElectronic health recordsLearning methodsHealth recordsComputer algorithmWidespread adoptionLearningMeaningful insightsMachineAlgorithmTechnologyTechniqueFrameworkInformationAdoptionDataApplications