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AJR Am J Roentgenol. 2019 Sep;213(3):568-574. doi: 10.2214/AJR.19.21512. Epub 2019 May 23.

Tackling the Radiological Society of North America Pneumonia Detection Challenge.

Author information

1
Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI.
2
Department of Diagnostic Imaging, The Warren Alpert Medical School of Brown University, Providence, RI.
3
Department of Medical Imaging, CISSS Lanaudière, Saint-Charles-Borromée, QC, Canada.
4
Department of Radiology, Université Laval, Quebec City, QC, Canada.
5
Department of Radiology, Keck School of Medicine of the University of Southern California, 1441 Eastlake Ave, Ste 2315B, Los Angeles, CA 90033.

Abstract

OBJECTIVE. We provide overviews of deep learning approaches used by two top-placing teams for the 2018 Radiological Society of North America (RSNA) Pneumonia Detection Challenge. CONCLUSION. Practical applications of deep learning techniques, as well as insights into the annotation of the data, were keys to success in accurately detecting pneumonia on chest radiographs for the competition.

KEYWORDS:

artificial intelligence; convolutional neural network; deep learning; detection; pneumonia

PMID:
31120793
DOI:
10.2214/AJR.19.21512

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