Deep Learning in Textural Medical Image Analysis

  • Aiga SuzukiEmail author
  • Hidenori Sakanashi
  • Shoji Kido
  • Hayaru Shouno
Part of the Intelligent Systems Reference Library book series (ISRL, volume 171)


One of the characteristics of medical image analysis is that several medical images are not in the structure domain like natural images but in the texture domain. This chapter introduces a new transfer learning method, called “two-stage feature transfer,” to analyze textural medical images by deep convolutional neural networks. In the process of the two-stage feature transfer learning, the models are successively pre-trained with both natural image dataset and textural image dataset to get a better feature representation which cannot be derived from either of these datasets. Experimental results show that the two-stage feature transfer improves the generalization performance of the convolutional neural network on a textural lung CT pattern classification. To explain the mechanism of a transfer learning on convolutional neural networks, this chapter also shows analysis results of the obtained feature representations by an activation visualization method, and by measuring the frequency response of trained neural networks, in both qualitative and quantitative ways, respectively. These results demonstrate that such successive transfer learning enables networks to grasp both structural and textural visual features and be helpful to extracting good features from the textural medical images.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Aiga Suzuki
    • 1
    • 2
    Email author
  • Hidenori Sakanashi
    • 1
    • 2
  • Shoji Kido
    • 3
  • Hayaru Shouno
    • 4
  1. 1.National Institute of Advanced Industrial Science and Technology (AIST)TsukubaJapan
  2. 2.University of TsukubaTsukubaJapan
  3. 3.Osaka UniversitySuita, OsakaJapan
  4. 4.University of Electro-CommunicationsChofuJapan

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