Development and Validation of a Deep Learning–Based Synthetic Bone-Suppressed Model for Pulmonary No...
Development and Validation of a Deep Learning–Based Synthetic Bone-Suppressed Model for Pulmonary Nodule Detection in Chest Radiographs
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Publisher
United States: American Medical Association
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Language
English
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Publisher
United States: American Medical Association
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Scope and Contents
Contents
Dual-energy chest radiography exhibits better sensitivity than single-energy chest radiography, partly due to its ability to remove overlying anatomical structures.
To develop and validate a deep learning-based synthetic bone-suppressed (DLBS) nodule-detection algorithm for pulmonary nodule detection on chest radiographs.
This decision analyt...
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Full title
Development and Validation of a Deep Learning–Based Synthetic Bone-Suppressed Model for Pulmonary Nodule Detection in Chest Radiographs
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TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9890286
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9890286
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ISSN
2574-3805
E-ISSN
2574-3805
DOI
10.1001/jamanetworkopen.2022.53820
