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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Dades bibliogràfiques
Publicat a:PLoS Med
Autors principals: 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.
Format: Artigo
Idioma:Inglês
Publicat: Public Library of Science 2018
Matèries:
Research Article
Accés en línia: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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