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Refining dataset curation methods for deep learning-based automated tuberculosis screening
BACKGROUND: The study objective was to determine whether unlabeled datasets can be used to further train and improve the accuracy of a deep learning system (DLS) for the detection of tuberculosis (TB) on chest radiographs (CXRs) using a two-stage semi-supervised approach. METHODS: A total of 111,622...
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| Publicado no: | J Thorac Dis |
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| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
AME Publishing Company
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7578485/ https://ncbi.nlm.nih.gov/pubmed/33145084 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.21037/jtd.2019.08.34 |
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