Skip to main content

Auto-classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model

  • Conference paper
  • First Online:
Computer Vision – ACCV 2018 Workshops (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11367))

Included in the following conference series:

Abstract

Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists. Based on the fact that fundus structure and vascular disorders are the main characteristics of retinal diseases, we propose a novel visual-assisted diagnosis hybrid model mixing the support vector machine (SVM) and deep neural networks (DNNs). Furthermore, we present a new clinical retina labels collection sorted by the professional ophthalmologist from the educational project Retina Image Bank, called EyeNet, for ophthalmology incorporating 52 retina diseases classes. Using EyeNet, our model achieves 90.40% diagnosis accuracy, and the model performance is comparable to the professional ophthalmologists (https://github.com/huckiyang/EyeNet2).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Tan, O., et al.: Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography. Ophthalmology 116, 2305–2314 (2009)

    Article  Google Scholar 

  2. Lalezary, M., et al.: Baseline optical coherence tomography predicts the development of glaucomatous change in glaucoma suspects. Am. J. Ophthalmol. 142, 576–582 (2006)

    Article  Google Scholar 

  3. Sharifi, M., Fathy, M., Mahmoudi, M.T.: A classified and comparative study of edge detection algorithms. In: International Conference on Information Technology: Coding and Computing, Proceedings, pp. 117–120. IEEE (2002)

    Google Scholar 

  4. Abràmoff, M.D., Garvin, M.K., Sonka, M.: Retinal imaging and image analysis. Rev. Biomed. Eng. 3, 169–208 (2010)

    Article  Google Scholar 

  5. Pizzarello, L., et al.: Vision 2020: the right to sight: a global initiative to eliminate avoidable blindness. Arch. Ophthalmol. 122, 615–620 (2004)

    Article  Google Scholar 

  6. Bhattacharya, S.: Watermarking digital images using fuzzy matrix compositions and (\(\alpha \), \(\beta \))-cut of fuzzy set. Int. J. Adv. Comput. 5, 135 (2014)

    Google Scholar 

  7. Lin, C.Y., Wu, M., Bloom, J.A., Cox, I.J., Miller, M.L., Lui, Y.M.: Rotation-, scale-, and translation-resilient public watermarking for images. In: Security and Watermarking of Multimedia Contents II, vol. 3971, pp. 90–99. International Society for Optics and Photonics (2000)

    Google Scholar 

  8. Cochocki, A., Unbehauen, R.: Neural Networks for Optimization and Signal Processing. Wiley, New York (1993)

    Google Scholar 

  9. Hannun, A., et al.: Deep speech: Scaling up end-to-end speech recognition. arXiv preprint arXiv:1412.5567 (2014)

  10. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)

    Google Scholar 

  11. Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250 (2016)

  12. Antol, S., et al.: VQA: Visual question answering. In: Proceedings of the ICCV, pp. 2425–2433 (2015)

    Google Scholar 

  13. Huang, J.H., Alfadly, M., Ghanem, B.: VQABQ: visual question answering by basic questions. arXiv:1703.06492 (2017)

  14. Huang, J.H., Dao, C.D., Alfadly, M., Ghanem, B.: A novel framework for robustness analysis of visual qa models. arXiv:1711.06232 (2017)

  15. Huang, J.H., Alfadly, M., Ghanem, B.: Robustness analysis of visual qa models by basic questions. arXiv:1709.04625 (2017)

  16. Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115 (2017)

    Article  Google Scholar 

  17. Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410 (2016)

    Article  Google Scholar 

  18. Rajpurkar, P., Hannun, A.Y., Haghpanahi, M., Bourn, C., Ng, A.Y.: Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv preprint arXiv:1707.01836 (2017)

  19. Rajpurkar, P., et al.: CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017)

  20. Grewal, M., Srivastava, M.M., Kumar, P., Varadarajan, S.: RADNET: radiologist level accuracy using deep learning for hemorrhage detection in ct scans. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 281–284. IEEE (2018)

    Google Scholar 

  21. Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318, 2199–2210 (2017)

    Article  Google Scholar 

  22. Gale, W., Oakden-Rayner, L., Carneiro, G., Bradley, A.P., Palmer, L.J.: Detecting hip fractures with radiologist-level performance using deep neural networks. arXiv preprint arXiv:1711.06504 (2017)

  23. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3462–3471. IEEE (2017)

    Google Scholar 

  24. Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. TMI 23, 501–509 (2004)

    Google Scholar 

  25. Gertych, A., Zhang, A., Sayre, J., Pospiech-Kurkowska, S., Huang, H.: Bone age assessment of children using a digital hand atlas. Comput. Med. Imaging Graph. 31, 322–331 (2007)

    Article  Google Scholar 

  26. Rajpurkar, P., et al.: Mura dataset: towards radiologist-level abnormality detection in musculoskeletal radiographs. arXiv preprint arXiv:1712.06957 (2017)

  27. Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, P.: The digital database for screening mammography. In: Digital Mammography, pp. 431–434 (2000)

    Google Scholar 

  28. Costa, J.A., Hero, A.: Classification constrained dimensionality reduction. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. Proceedings, (ICASSP’05), vol. 5, pp. 1077. IEEE (2005)

    Google Scholar 

  29. Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, London (2013)

    MATH  Google Scholar 

  30. Jimenez, L.O., Landgrebe, D.A.: Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 28, 39–54 (1998)

    Article  Google Scholar 

  31. Yang, Z., He, X., Gao, J., Deng, L., Smola, A.: Stacked attention networks for image question answering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 21–29 (2016)

    Google Scholar 

  32. Trier, Ø.D., Jain, A.K., Taxt, T.: Feature extraction methods for character recognition-a survey. Pattern Recogn. 29, 641–662 (1996)

    Article  Google Scholar 

  33. Srihari, R., Li, W.: Information extraction supported question answering. Technical report, Cymfony Net Inc., Williamsville NY (1999)

    Google Scholar 

  34. Somers, H.: Example-based machine translation. Mach. Transl. 14, 113–157 (1999)

    Article  Google Scholar 

  35. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)

  36. Ciresan, D., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. In: Advances in neural information processing systems, pp. 2843–2851 (2012)

    Google Scholar 

  37. Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 447–456 (2015)

    Google Scholar 

  38. Seyedhosseini, M., Sajjadi, M., Tasdizen, T.: Image segmentation with cascaded hierarchical models and logistic disjunctive normal networks. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 2168–2175. IEEE(2013)

    Google Scholar 

  39. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  40. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431–3440 (2015)

    Google Scholar 

  41. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)

  42. Khurana, A.: Comprehensive Ophthalmology. New Age International Ltd. (2007)

    Google Scholar 

  43. Rezaee, K., Haddadnia, J., Tashk, A.: Optimized clinical segmentation of retinal blood vessels by using combination of adaptive filtering, fuzzy entropy and skeletonization. Appl. Soft Comput. 52, 937–951 (2017)

    Article  Google Scholar 

  44. Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings CVPR 1991, pp. 586–591. IEEE (1991)

    Google Scholar 

  45. Lyons, M.J., Budynek, J., Akamatsu, S.: Automatic classification of single facial images. IEEE Trans. Pattern Anal. Mach. Intell. 21, 1357–1362 (1999)

    Article  Google Scholar 

  46. Wu, J., Zhou, Z.H.: Face recognition with one training image per person. Pattern Recogn. Lett. 23, 1711–1719 (2002)

    Article  Google Scholar 

  47. Moghaddam, B., Wahid, W., Pentland, A.: Beyond eigenfaces: probabilistic matching for face recognition. In: Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998. Proceedings, pp. 30–35. IEEE (1998)

    Google Scholar 

  48. Akram, M.U., Tariq, A., Khan, S.A.: Retinal recognition: Personal identification using blood vessels. In: 2011 International Conference for Internet Technology and Secured Transactions (ICITST), pp. 180–184. IEEE (2011)

    Google Scholar 

  49. Kuo, B.C., Ho, H.H., Li, C.H., Hung, C.C., Taur, J.S.: A kernel-based feature selection method for SVM with RBF kernel for hyperspectral image classification. IEEE J.Sel. Top. Appl. Earth Observ. Remote Sens. 7, 317–326 (2014)

    Article  Google Scholar 

  50. Crick, R.P., Khaw, P.T.: A Textbook of Clinical Ophthalmology: A Practical Guide to Disorders of the Eyes and Their Management. World Scientific

    Google Scholar 

  51. Akram, I., Rubinstein, A.: Common retinal signs. an overview. Optometry Today (2005)

    Google Scholar 

  52. Tang, S., Huang, L., Wang, Y., Wang, Y.: Contrast-enhanced ultrasonography diagnosis of fundal localized type of gallbladder adenomyomatosis. BMC Gastroenterol. 15, 99 (2015)

    Article  Google Scholar 

  53. Noyel, G., Thomas, R., Bhakta, G., Crowder, A., Owens, D., Boyle, P.: Superimposition of eye fundus images for longitudinal analysis from large public health databases. Biomed. Phys. Eng. Express 3, 045015 (2017)

    Article  Google Scholar 

  54. : Retina Image Bank: A project from the American Society of Retina Specialists. http://imagebank.asrs.org/about. Accessed 30 June 2018

  55. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in NIPS, pp. 1097–1105 (2012)

    Google Scholar 

  56. Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: CVPR, pp. 5987–5995. IEEE (2017)

    Google Scholar 

  57. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-2010), pp. 807–814 (2010)

    Google Scholar 

  58. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition

    Google Scholar 

  59. Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \(<\)0.5 MB model size. arXiv:1602.07360 (2016)

  60. Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: NIPS, pp. 3320–3328 (2014)

    Google Scholar 

  61. Yang, C.H.H., et al.: A novel hybrid machine learning model for auto-classification of retinal diseases. arXiv preprint arXiv:1806.06423 (2018)

  62. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. TIST 2, 27 (2011)

    Article  Google Scholar 

  63. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to C.-H. Huck Yang or Fangyu Liu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Huck Yang, CH. et al. (2019). Auto-classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model. In: Carneiro, G., You, S. (eds) Computer Vision – ACCV 2018 Workshops. ACCV 2018. Lecture Notes in Computer Science(), vol 11367. Springer, Cham. https://doi.org/10.1007/978-3-030-21074-8_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-21074-8_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21073-1

  • Online ISBN: 978-3-030-21074-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics