Gastric Cancer Detection from Endoscopic Images Using Synthesis by GAN

  • Teppei KanayamaEmail author
  • Yusuke Kurose
  • Kiyohito Tanaka
  • Kento Aida
  • Shin’ichi Satoh
  • Masaru Kitsuregawa
  • Tatsuya Harada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11768)


Datasets for training gastric cancer detection models are usually imbalanced, because the number of available images showing lesions is limited. This imbalance can be a serious obstacle to realizing a high-performance automatic gastric cancer detection system. In this paper, we propose a method that lessens this dataset bias by generating new images using a generative model. The generative model synthesizes an image from two images in a dataset. The synthesis network can produce realistic images, even if the dataset of lesion images is small. In our experiment, we trained gastric cancer detection models using the synthesized images. The results show that the performance of the system was improved.


Endoscopy image Generative adversarial networks Gastric cancer detection Dataset bias 



This work was supported by a Grant for ICT infrastructure establishment and implementation of artificial intelligence for clinical and medical research from the Japan Agency of Medical Research and Development AMED (JP18lk1010028).

Supplementary material

490279_1_En_59_MOESM1_ESM.pdf (403 kb)
Supplementary material 1 (pdf 403 KB)


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Teppei Kanayama
    • 1
    Email author
  • Yusuke Kurose
    • 1
  • Kiyohito Tanaka
    • 2
  • Kento Aida
    • 3
  • Shin’ichi Satoh
    • 3
  • Masaru Kitsuregawa
    • 4
    • 5
  • Tatsuya Harada
    • 1
    • 3
  1. 1.Graduate School of Information Science and TechnologyThe University of TokyoTokyoJapan
  2. 2.Kyoto Second Red Cross HospitalKyotoJapan
  3. 3.Research Center for Medical BigdataNational Institute of InformaticsTokyoJapan
  4. 4.Institute of Industrial ScienceThe University of TokyoTokyoJapan
  5. 5.National Institute of InformaticsTokyoJapan

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