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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2020
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| Materias: | |
| 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 |