Classification of Findings with Localized Lesions in Fundoscopic Images Using a Regionally Guided CNN

  • Jaemin Son
  • Woong Bae
  • Sangkeun Kim
  • Sang Jun Park
  • Kyu-Hwan JungEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11039)


Fundoscopic images are often investigated by ophthalmologists to spot abnormal lesions to make diagnoses. Recent successes of convolutional neural networks are confined to diagnoses of few diseases without proper localization of lesion. In this paper, we propose an efficient annotation method for localizing lesions and a CNN architecture that can classify an individual finding and localize the lesions at the same time. Also, we introduce a new loss function to guide the network to learn meaningful patterns with the guidance of the regional annotations. In experiments, we demonstrate that our network performed better than the widely used network and the guidance loss helps achieve higher AUROC up to \(4.1\%\) and superior localization capability.


  1. 1.
    Bae, J.P., Kim, K.G., Kang, H.C., Jeong, C.B., Park, K.H., Hwang, J.M.: A study on hemorrhage detection using hybrid method in fundus images. J. Dig. Imaging 24(3), 394–404 (2011)CrossRefGoogle Scholar
  2. 2.
    Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. arXiv preprint arXiv:1606.00915 (2016)
  3. 3.
    Gargeya, R., Leng, T.: Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7), 962–969 (2017)CrossRefGoogle Scholar
  4. 4.
    Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)Google Scholar
  5. 5.
    Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)CrossRefGoogle Scholar
  6. 6.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  7. 7.
    Keskar, N.S., Mudigere, D., Nocedal, J., Smelyanskiy, M., Tang, P.T.P.: On large-batch training for deep learning: generalization gap and sharp minima. arXiv preprint arXiv:1609.04836 (2016)
  8. 8.
    Köse, C., ŞEvik, U., İKibaş, C., Erdöl, H.: Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images. Comput. Methods Programs Biomed. 107(2), 274–293 (2012)CrossRefGoogle Scholar
  9. 9.
    Rapantzikos, K., Zervakis, M., Balas, K.: Detection and segmentation of drusen deposits on human retina: potential in the diagnosis of age-related macular degeneration. Med. Image Anal. 7(1), 95–108 (2003)CrossRefGoogle Scholar
  10. 10.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  11. 11.
    Sasaki, M., Kawasaki, R., Noonan, J.E., Wong, T.Y., Lamoureux, E., Wang, J.J.: Quantitative measurement of hard exudates in patients with diabetes and their associations with serum lipid levels. Invest. Ophthalmol. Vis. Sci. 54(8), 5544–5550 (2013)CrossRefGoogle Scholar
  12. 12.
    Takahashi, H., Tampo, H., Arai, Y., Inoue, Y., Kawashima, H.: Applying artificial intelligence to disease staging: deep learning for improved staging of diabetic retinopathy. PLoS One 12(6), e0179790 (2017)CrossRefGoogle Scholar
  13. 13.
    Ting, D.S.W., et al.: Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318(22), 2211–2223 (2017)CrossRefGoogle Scholar
  14. 14.
    Wilkinson, C., et al.: Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110(9), 1677–1682 (2003)CrossRefGoogle Scholar
  15. 15.
    Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jaemin Son
    • 1
  • Woong Bae
    • 1
  • Sangkeun Kim
    • 1
  • Sang Jun Park
    • 2
  • Kyu-Hwan Jung
    • 1
    Email author
  1. 1.VUNO Inc.SeoulKorea
  2. 2.Department of OphthalmologySeoul National University Bundang Hospital and Seoul National University College of MedicineSeongnamKorea

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