Ruben De Man
About
Research
Publications
2019
Hybrid Neural Networks for Mortality Prediction from LDCT Images
Yan P, Guo H, Wang G, De Man R, Kalra MK. Hybrid Neural Networks for Mortality Prediction from LDCT Images. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2019, 00: 6243-6246. PMID: 31947269, DOI: 10.1109/embc.2019.8857180.Peer-Reviewed Original ResearchConceptsLow-dose CTMortality predictionLung cancer patientsCancer mortality predictionsMortality risk predictionLung cancer subjectsCause mortalityHigh morbidityCancer patientsLung cancerImaging featuresCardiovascular diseaseCancer subjectsHigh riskMortality riskRisk scoreMortality rateClinical practiceDeadly diseaseRisk predictionLDCT imagesDiseaseRiskSame populationScoring methodComparison of deep learning and human observer performance for lesion detection and characterization
De Man R, Gang G, Li X, Wang G. Comparison of deep learning and human observer performance for lesion detection and characterization. Proceedings Of SPIE--the International Society For Optical Engineering 2019, 11072: 110721f-110721f-5. DOI: 10.1117/12.2532331.Peer-Reviewed Original ResearchComparison of deep learning and human observer performance for detection and characterization of simulated lesions
De Man R, Gang GJ, Li X, Wang G. Comparison of deep learning and human observer performance for detection and characterization of simulated lesions. Journal Of Medical Imaging 2019, 6: 025503-025503. PMID: 31263738, PMCID: PMC6586983, DOI: 10.1117/1.jmi.6.2.025503.Peer-Reviewed Original Research
2018
Multifactorial Analysis of Mortality in Screening Detected Lung Cancer
Digumarthy SR, De Man R, Canellas R, Otrakji A, Wang G, Kalra MK. Multifactorial Analysis of Mortality in Screening Detected Lung Cancer. Journal Of Oncology 2018, 2018: 1296246. PMID: 29861726, PMCID: PMC5976935, DOI: 10.1155/2018/1296246.Peer-Reviewed Original ResearchCoronary artery calcificationNational Lung Screening TrialLow-dose CTBody mass indexLung cancerFat attenuationMuscle massLow-dose chest CTLung cancer mortalitySeverity of emphysemaLung cancer stageNLST trialNonsurvivor groupArtery calcificationSmoking historyMass indexCancer mortalityChest CTCancer stageSurvivor groupScreening TrialSurvival timeEmphysemaPatientsCancer
2016
Upper-Bound on Dose Reduction in CT Reconstruction for Nodule Detection
De Man R, Wang G, Kalra M, Otrakji A, Hsieh S, Pelc N. Upper-Bound on Dose Reduction in CT Reconstruction for Nodule Detection. IEEE Access 2016, 4: 4247-4253. DOI: 10.1109/access.2016.2592941.Peer-Reviewed Original ResearchDetection accuracyMajor CT vendorsRaw data domainMedical diagnosis toolCT reconstructionIterative image reconstructionAdditional prior informationSpecific location informationLocation informationData domainNodule detectionCT data setsDetection taskImage reconstructionRaw dataCT vendorsReconstruction algorithmReconstruction solutionCase performancePrior informationDiagnosis toolData setsImage qualityUpper boundsAchievable dose reduction