Advertisement

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)

Abstract

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.

Keywords

Endoscopy image Generative adversarial networks Gastric cancer detection Dataset bias 

Notes

Acknowledgements

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)

References

  1. 1.
    Baur, C., Albarqouni, S., Navab, N.: MelanoGANs: high resolution skin lesion synthesis with GANs. arXiv:1804.04338 (2018)
  2. 2.
    Beers, A., et al.: High-resolution medical image synthesis using progressively grown generative adversarial networks. arXiv:1805.03144 (2018)
  3. 3.
    Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing (2018). http://www.sciencedirect.com/science/article/pii/S0925231218310749
  4. 4.
    Goodfellow, I., et al.: Generative adversarial nets. In: NIPS (2014). http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
  5. 5.
    Hayakawa, A., et al.: Gastric cancer detection for gastroenterological endoscopy with local and multi-scale global information. In: CARS (2019)Google Scholar
  6. 6.
    Hirasawa, T., et al.: Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 21, 653–660 (2018)CrossRefGoogle Scholar
  7. 7.
    Iizuka, S., Simo-Serra, E., Ishikawa, H.: Globally and locally consistent image completion. ACM Trans. Graph. 36, 107 (2017)CrossRefGoogle Scholar
  8. 8.
    Kawahara, J., Hamarneh, G.: Multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers. In: MICCAI (2016)Google Scholar
  9. 9.
    Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46448-0_2 CrossRefGoogle Scholar
  10. 10.
    Lo, Y.C., et al.: Glomerulus detection on light microscopic images of renal pathology with the faster R-CNN. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) Neural Information Processing (2018)CrossRefGoogle Scholar
  11. 11.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: ICLR (2016)Google Scholar
  12. 12.
    Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv:1804.02767 (2018)
  13. 13.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)Google Scholar
  14. 14.
    Shen, W., Zhou, M., Yang, F., Yang, C., Tian, J.: Multi-scale convolutional neural networks for lung nodule classification. In: IPMI (2015)Google Scholar
  15. 15.
    Xian, W., et al.: TextureGAN: controlling deep image synthesis with texture patches. In: CVPR (2018)Google Scholar
  16. 16.
    Xiao, T., Zhang, C., Zha, H.: Learning to detect anomalies in surveillance video. IEEE Signal Process. Lett. 22, 1477–1481 (2015) CrossRefGoogle Scholar
  17. 17.
    Xiao T., Zhang C., Z.H.W.F.: Factorization and spatio-temporal pyramid. In: ACCV (2014)Google Scholar
  18. 18.
    Yi, X., Walia, E., Babyn, P.: Generative adversarial network in medical imaging: a review. Med. Syst. (2018)Google Scholar
  19. 19.
    Zhang, Z., Xie, Y., Yang, L.: Photographic text-to-image synthesis with a hierarchically-nested adversarial network. In: CVPR (2018)Google Scholar

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

Personalised recommendations