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Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs
We aimed to develop a deep learning algorithm detecting 10 common abnormalities (DLAD-10) on chest radiographs, and to evaluate its impact in diagnostic accuracy, timeliness of reporting and workflow efficacy. DLAD-10 was trained with 146 717 radiographs from 108 053 patients using a ResNet34-based...
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| Publié dans: | Eur Respir J |
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| Auteurs principaux: | , , , , , , , |
| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
European Respiratory Society
2021
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| Sujets: | |
| Accès en ligne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8134811/ https://ncbi.nlm.nih.gov/pubmed/33243843 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1183/13993003.03061-2020 |
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