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Assistive Diagnosis in Opthalmology Using Deep Learning-Based Image Retrieval

  • Azeem BootwalaEmail author
  • Katharina Breininger
  • Andreas Maier
  • Vincent Christlein
Conference paper
  • 52 Downloads
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Image-based diagnosis of the human eye is crucial for the early detection of several diseases in ophthalmology. In this work, we investigate the possibility to use image retrieval to support the diagnosis of diabetic retinopathy. To this end, we evaluate different feature learning techniques. In particular, we evaluate the performance of cost functions specialized for metric learning, namely, contrastive loss, triplet loss and histogram loss, and compare them with the classification crossentropy loss. Additionally, we train the network on images graded by diabetic retinopathy severity and transfer the knowledge learned, to retrieve images that are graded by diabetic macular edema severity and evaluate our algorithm on three different datasets. For the task of detecting referable/non-referable diabetic retinopathy, we achieve a sensitivity of 0.84 and specificity of 0.88 on the Kaggle dataset using histogram loss. On the Messidor dataset, we achieve a sensitivity and specificity score of 0.79 and 0.84, respectively.

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Literatur

  1. 1.
    World Health Organization. Global report on diabetes; 2016.Google Scholar
  2. 2.
    Schroff F, Kalenichenko D, Philbin J. FaceNet: a unified embedding for face recognition and clustering. In: Proc CVPR; 2015. p. 815–823.Google Scholar
  3. 3.
    Zhang X, Felix XY, Kumar S, et al. Learning spread-out local feature descriptors. In: Proc ICCV; 2017. p. 4605–4613.Google Scholar
  4. 4.
    Hadsell R, Chopra S, LeCun Y. Dimensionality reduction by learning an invariant mapping. In: Proc CVPR. vol. 2; 2006. p. 1735–1742.Google Scholar
  5. 5.
    Ustinova E, Lempitsky V. Learning deep embeddings with histogram loss. In: Proc NIPS; 2016. p. 4170–4178.Google Scholar
  6. 6.
    Krause J, Gulshan V, Rahimy E, et al. Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. Ophthalmology. 2018 Aug;125(8):1264–1272.Google Scholar
  7. 7.
    EyePACS. Diabetic retinopathy detection;. Accessed: 2019-11-1. https://www.kaggle.com/c/diabetic-retinopathy-detection.
  8. 8.
    Decenciére E, Zhang X, Cazuguel G, et al. Feedback on a publicly distributed database: the messidor database. Image Anal & Stereology. 2014 Aug;33(3):231–234.Google Scholar
  9. 9.
    Porwal P, Pachade S, Kamble R, et al. Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. Data. 2018;3(3).Google Scholar
  10. 10.
    Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv e-prints. 2014; p. arXiv:1409.1556.

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  • Azeem Bootwala
    • 1
    Email author
  • Katharina Breininger
    • 1
  • Andreas Maier
    • 1
  • Vincent Christlein
    • 1
  1. 1.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-NürnbergErlangen-NürnbergDeutschland

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