Art
J-GLOBAL ID:202202229714389100   Reference number:22A1170245

Prediction of Pulmonary Fibrosis Based on X-Rays by Deep Neural Network

深層ニューラルネットワークによるX線に基づく肺線維症の予測【JST・京大機械翻訳】
Author (5):
Material:
Volume: 2022  Page: Null  Publication year: 2022 
JST Material Number: U7772A  ISSN: 2040-2295  Document type: Article
Article type: 原著論文  Country of issue: United Kingdom (GBR)  Language: ENGLISH (EN)
Abstract/Point:
Abstract/Point
Japanese summary of the article(about several hundred characters).
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As a fatal lung disease, pulmo...
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Respiratory system diagnosis 
Reference (15):
  • M. K. Glassberg, "Overview of idiopathic pulmonary fibrosis evidence-based guidelines and recent developments in the treatment landscape," American Journal of Managed Care, vol. 25, pp. S195-S203, 2019.
  • C. M. Liu, H. R. Cai, "Progress in treatment for idiopathic pulmonary fibrosis," Clinical Medication Journal, vol. 13, no. 3, pp. 1-4, 2015.
  • X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, R. M. Summers, "ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases," 2017, https://arxiv.org/abs/1705.02315.
  • P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, D. Ding, A. Bagul, C. Langlotz, K. Shpanskaya, M P. Lungren, A. Y. Ng, "CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning," 2017, https://arxiv.org/abs/1711.05225.
  • L. Zhao, T. Yang, J. Zhang, Z. Chen, Y. Yang, Z. Jane Wang, "Co-learning non-negative correlated and uncorrelated features for multi-view data," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, pp. 1486-1496, 2021.
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