Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

BACKGROUND: Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based di...

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Detalhes bibliográficos
Publicado no:PLoS Med
Main Authors: Rajpurkar, Pranav, Irvin, Jeremy, Ball, Robyn L., Zhu, Kaylie, Yang, Brandon, Mehta, Hershel, Duan, Tony, Ding, Daisy, Bagul, Aarti, Langlotz, Curtis P., Patel, Bhavik N., Yeom, Kristen W., Shpanskaya, Katie, Blankenberg, Francis G., Seekins, Jayne, Amrhein, Timothy J., Mong, David A., Halabi, Safwan S., Zucker, Evan J., Ng, Andrew Y., Lungren, Matthew P.
Formato: Artigo
Idioma:Inglês
Publicado em: Public Library of Science 2018
Assuntos:
Research Article
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC6245676/
https://ncbi.nlm.nih.gov/pubmed/30457988
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pmed.1002686
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