2023
Digital Twins for Radiation Oncology
Jensen J, Deng J. Digital Twins for Radiation Oncology. 2023, 989-993. DOI: 10.1145/3543873.3587688.Peer-Reviewed Original ResearchDigital twin technologyDigital twinTwin technologyDigital twin networkModern cryptographic techniquesClinical decision-making supportDecision-making supportCryptographic techniquesVirtual clinical trialsTwin networkConventional machineGeneralized architectureFuture scientific advancesNatural applicationModeling techniquesPatient dataTechnologyMultiple institutionsLarge numberArchitectureMachineMultiscale modeling techniqueNetworkApplicationsTechnique
2020
Radiomics at a Glance: A Few Lessons Learned from Learning Approaches
Capobianco E, Deng J. Radiomics at a Glance: A Few Lessons Learned from Learning Approaches. Cancers 2020, 12: 2453. PMID: 32872466, PMCID: PMC7563283, DOI: 10.3390/cancers12092453.Peer-Reviewed Original ResearchMedical image featuresData analyticsDeep learningMedical imagesImage featuresPrediction taskLearning methodsLearning approachComplex taskHybrid learningInference approachLarge volumesRadiomicsTaskLearningImagesAnalyticsScalabilityUltimate goalMachineAlgorithmClassificationAccuracyProcessingOptimizationRobust Machine Learning for Colorectal Cancer Risk Prediction and Stratification
Nartowt BJ, Hart GR, Muhammad W, Liang Y, Stark GF, Deng J. Robust Machine Learning for Colorectal Cancer Risk Prediction and Stratification. Frontiers In Big Data 2020, 3: 6. PMID: 33693381, PMCID: PMC7931964, DOI: 10.3389/fdata.2020.00006.Peer-Reviewed Original ResearchArtificial neural networkNeural networkOne-hot encodingSupport vector machineNational Health Interview SurveyExpectation maximization imputationNaive BayesSupervised machineRobust machineVector machineRandom forestDecision treeCRC riskColorectal cancerPLCO datasetMachineScreening datasetsNetworkColorectal cancer risk predictionImputation methodsPrevention of CRCDatasetHealth Interview SurveyListwise deletionMethod combination