論文

査読有り 国際誌
2020年

Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation.

Advances in experimental medicine and biology
  • Shoji Kido
  • ,
  • Yasushi Hirano
  • ,
  • Shingo Mabu

1213
開始ページ
47
終了ページ
58
記述言語
英語
掲載種別
論文集(書籍)内論文
DOI
10.1007/978-3-030-33128-3_3

Image-based computer-aided diagnosis (CAD) algorithms by the use of convolutional neural network (CNN) which do not require the image-feature extractor are powerful compared with conventional feature-based CAD algorithms which require the image-feature extractor for classification of lung abnormalities. Moreover, computer-aided detection and segmentation algorithms by the use of CNN are useful for analysis of lung abnormalities. Deep learning will improve the performance of CAD systems dramatically. Therefore, they will change the roles of radiologists in the near future. In this article, we introduce development and evaluation of such image-based CAD algorithms for various kinds of lung abnormalities such as lung nodules and diffuse lung diseases.

リンク情報
DOI
https://doi.org/10.1007/978-3-030-33128-3_3
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/32030662
Scopus
https://www.scopus.com/record/display.uri?eid=2-s2.0-85079083090&origin=inward
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85079083090&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85079083090&origin=inward

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