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Deep Learning–Based Digitally Reconstructed Tomography of the Chest in the Evaluation of Solitary Pulmonary Nodules: A Feasibility Study
Ayis Pyrros
, Andrew Chen
,
Jorge Mario Rodríguez-Fernández
, Stephen M. Borstelmann
, Patrick A. Cole
, Jeanne Horowitz
, Jonathan Chung
, Paul Nikolaidis
, Viveka Boddipalli
, Nasir Siddiqui
, Melinda Willis
, Adam Eugene Flanders
, Sanmi Koyejo
Research output
:
Contribution to journal
›
Article
›
peer-review
4
Scopus citations
Overview
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Dive into the research topics of 'Deep Learning–Based Digitally Reconstructed Tomography of the Chest in the Evaluation of Solitary Pulmonary Nodules: A Feasibility Study'. Together they form a unique fingerprint.
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Keyphrases
Tomography
100%
Solitary Pulmonary Nodule
100%
Diagnostic Performance
27%
Computed Tomography
18%
Interobserver Agreement
18%
Opacity
18%
Tomography Image
18%
Pulmonary Nodule Detection
18%
Single Institution
9%
Non-contrast
9%
Chest Radiograph
9%
Chest CT
9%
Lateral Radiograph
9%
Radiologists
9%
Area under the Receiver Operating Characteristic Curve
9%
Computed Tomography Images
9%
Clinical Feasibility
9%
Nodule Size
9%
Nodule Location
9%
Helical Computed Tomography
9%
Deep Learning
9%
Deep Learning Model
9%
Deep Learning Algorithm
9%
Scanogram
9%
Planar Radiography
9%
Medicine and Dentistry
Tomography
100%
Lung Nodule
100%
Computer Assisted Tomography
27%
Diagnostic Performance
27%
Retrospective Study
9%
Thorax Radiography
9%
Spiral Computed Tomography
9%