Performance of Qure.ai automatic classifiers against a large annotated database of patients with diverse forms of tuberculosis

Availability of trained radiologists for fast processing of CXRs in regions burdened with tuberculosis always has been a challenge, affecting both timely diagnosis and patient monitoring. The paucity of annotated images of lungs of TB patients hampers attempts to apply data-oriented algorithms for r...

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Detalles Bibliográficos
Autores principales: Engle, Eric, Gabrielian, Andrei, Long, Alyssa, Hurt, Darrell E., Rosenthal, Alex
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6980594/
https://www.ncbi.nlm.nih.gov/pubmed/31978149
http://dx.doi.org/10.1371/journal.pone.0224445

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6980594/
https://www.ncbi.nlm.nih.gov/pubmed/31978149
http://dx.doi.org/10.1371/journal.pone.0224445

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