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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Detalles Bibliográficos
Autores principales: Kim, Tae Kyung, Yi, Paul H., Hager, Gregory D., Lin, Cheng Ting
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2020
Materias:
Original Article on Role of Precision Imaging in Thoracic Disease
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7578485/
https://www.ncbi.nlm.nih.gov/pubmed/33145084
http://dx.doi.org/10.21037/jtd.2019.08.34

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7578485/
https://www.ncbi.nlm.nih.gov/pubmed/33145084
http://dx.doi.org/10.21037/jtd.2019.08.34

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