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Samuel G. Armato Iii
Also published under:S. G. Armato, Samuel G. Armato
Affiliation
Department of Radiology, The University of Chicago, Chicago, IL, USA
Topic
Medical Imaging,Big Data,Computed Tomography,Active Contour,Adjacent Points,Advanced Computational Methods,Artificial Intelligence Systems,Big Data Analytics,Boundary Length,Candidate Features,Clinical Reports,Clinical Use,Computer Technology,Computer-aided Diagnosis,Concavity,Content-based Image Retrieval,Continuous Integration,Convex Hull,Data Integration,Data Sources,Depth Threshold,Diagnostic Process,Domain Experts,Electronic Health Records,Extract Meaningful Information,General Method,Gray-level Images,Grayscale,Health Information Exchange,Healthcare Domain,Identification Method,Identification Of Candidates,Identification Of Patients,Image Acquisition,Image Acquisition Parameters,Image Processing,Image Retrieval,Imaging Device,Imaging Studies,Incorrect Identification,Integration Of Big Data,Integration Of Data Sources,Interpretation Of Assessment,Line Segment,Linear Interpolation,Lung Nodules,Magnetic Resonance Imaging,Magnitude Data,Medical Records,Metadata,
Biography
Samuel G. Armato, III, received the B.A degree in physics in 1987 and the Ph.D. degree in medical physics in 1997, both from The University of Chicago, Chicago, IL. His doctoral research involved automated lung segmentation in posteroanterior and lateral chest radiographs and applications of segmentation to the quantitative analysis of lung fields and registration with radionuclide lung scan images.
He is an Assistant Professor of Radiology at The University of Chicago. His current research involves the investigation of computer-aided diagnostic methods in thoracic CT scans, including lung segmentation, lung nodule detection and classification, and pleural analysis.
Dr. Armato serves as a reviewer for a number of granting agencies and scientific journals.
He is an Assistant Professor of Radiology at The University of Chicago. His current research involves the investigation of computer-aided diagnostic methods in thoracic CT scans, including lung segmentation, lung nodule detection and classification, and pleural analysis.
Dr. Armato serves as a reviewer for a number of granting agencies and scientific journals.