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Retinopathy Diagnosis Using Semi-supervised Multi-channel Generative Adversarial Network

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11855)

Abstract

Various kinds of retinopathy are the leading causes of blindness in human being, and with the rapid development of fundus images (FI) analysis in recent years, deep learning has became the focus while using Computer-Aided-Diagnosis (CAD) system to diagnose retinopathy. However, the conventional deep learning method usually rely on sufficient number of labeled FI, but the cost of acquiring enough labeled FI is too expensive, in addition the difficulty in identifying the tiny lesions and the randomness of lesions distribution also brings great challenge to CAD via deep learning. Therefore, this paper proposes a semi-supervised multi-channel generative adversarial network (GAN), which can reasonably utilize the unlabeled images and generate new samples to reduce the dependence on the labeled images, meanwhile we introduce into a general feature extraction strategy to avoid the problem of image valid information disappearance caused by downsampling, and improve the robustness of the generative and discriminant network. The experimental results show that our proposed network boosts the classification accuracy by 10.5% compared with the control network using conventional method and reaches the highest of 88.9%.

Keywords

Retinopathy Semi-supervised Multi-channel Generative adversarial network General feature extraction 

References

  1. 1.
    Bourne, R.R., et al.: Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis. Lancet Glob. Health 5(9), e888–e897 (2017)CrossRefGoogle Scholar
  2. 2.
    Budai, A., Bock, R., Maier, A., Hornegger, J., Michelson, G.: Robust vessel segmentation in fundus images. Int. J. Biomed. Imaging 2013 (2013) CrossRefGoogle Scholar
  3. 3.
    Buslaev, A., Parinov, A., Khvedchenya, E., Iglovikov, V.I., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. ArXiv e-prints (2018)Google Scholar
  4. 4.
    Denton, E.L., Chintala, S., Fergus, R., et al.: Deep generative image models using a laplacian pyramid of adversarial networks. In: Advances in Neural Information Processing Systems, pp. 1486–1494 (2015)Google Scholar
  5. 5.
    Giancardo, L., et al.: Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Med. Image Anal. 16(1), 216–226 (2012)CrossRefGoogle Scholar
  6. 6.
    Goodfellow, I.J., et al.: Generative adversarial nets. In: International Conference on Neural Information Processing Systems (2014)Google Scholar
  7. 7.
    Kauppi, T., et al.: The DIARETDB1 diabetic retinopathy database and evaluation protocol. BMVC 1, 1–10 (2007)Google Scholar
  8. 8.
    Kauppi, T., et al.: DIARETDB0: evaluation database and methodology for diabetic retinopathy algorithms. Mach. Vis. Pattern Recogn. Res. Group Lappeenranta Univ. Technol. Finland 73, 1–17 (2006)Google Scholar
  9. 9.
    Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRefGoogle Scholar
  10. 10.
    Odena, A.: Semi-supervised learning with generative adversarial networks. arXiv preprint. arXiv:1606.01583 (2016)
  11. 11.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2009)CrossRefGoogle Scholar
  12. 12.
    Priya, R., Aruna, P.: Diagnosis of diabetic retinopathy using machine learning techniques. ICTACT J. Soft Comput. 3(4), 563–575 (2013)CrossRefGoogle Scholar
  13. 13.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint. arXiv:1511.06434 (2015)
  14. 14.
    Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems, pp. 2234–2242 (2016)Google Scholar
  15. 15.
    Sutskever, I., Jozefowicz, R., Gregor, K., Rezende, D., Lillicrap, T., Vinyals, O.: Towards principled unsupervised learning. arXiv preprint. arXiv:1511.06440 (2015)
  16. 16.
    Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)Google Scholar
  17. 17.
    Warde-Farley, D., Goodfellow, I.: 11 adversarial perturbations of deep neural networks. Perturbations Optim. Stat. 311 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science CenterShenzhen UniversityShenzhenChina
  2. 2.Ningbo Institute of Industrial Technology, Chinese Academy of SciencesNingboChina

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