論文

査読有り
2020年

Investigating the Effects of Transfer Learning on ROI-based Classification of Chest CT Images: A Case Study on Diffuse Lung Diseases.

J. Signal Process. Syst.
  • Shingo Mabu
  • ,
  • Ami Atsumo
  • ,
  • Shoji Kido
  • ,
  • Takashi Kuremoto
  • ,
  • Yasushi Hirano

92
3
開始ページ
307
終了ページ
313
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1007/s11265-019-01499-w

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Research on Computer-Aided Diagnosis (CAD) of medical images has been actively conducted to support decisions of radiologists. Since deep learning has shown distinguished abilities in classification, detection, segmentation, etc. in various problems, many studies on CAD have been using deep learning. One of the reasons behind the success of deep learning is the availability of large application-specific annotated datasets. However, it is quite tough work for radiologists to annotate hundreds or thousands of medical images for deep learning, and thus it is difficult to obtain large scale annotated datasets for various organs and diseases. Therefore, many techniques that effectively train deep neural networks have been proposed, and one of the techniques is transfer learning. This paper focuses on transfer learning and especially conducts a case study on ROI-based opacity classification of diffuse lung diseases in chest CT images. The aim of this paper is to clarify what characteristics of the datasets for pre-training and what kinds of structures of deep neural networks for fine-tuning contribute to enhance the effectiveness of transfer learning. In addition, the numbers of training data are set at various values and the effectiveness of transfer learning is evaluated. In the experiments, nine conditions of transfer learning and a method without transfer learning are compared to analyze the appropriate conditions. From the experimental results, it is clarified that the pre-training dataset with more (various) classes and the compact structure for fine-tuning show the best accuracy in this work.

リンク情報
DOI
https://doi.org/10.1007/s11265-019-01499-w
Scopus
https://www.scopus.com/record/display.uri?eid=2-s2.0-85077699683&origin=inward
Dblp Url
https://dblp.uni-trier.de/db/journals/vlsisp/vlsisp92.html#MabuAKKH20
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85077699683&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85077699683&origin=inward

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