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AI-based AMD Analysis: A Review of Recent Progress

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Computer Vision – ACCV 2018 Workshops (ACCV 2018)

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

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Abstract

Since 2016 much progress has been made in the automatic analysis of age related macular degeneration (AMD). Much of it was dedicated to the classification of referable vs. non-referable AMD, fine-grained AMD severity classification, and assessing the five-year risk of progression to the severe form of AMD. Here we review these developments, the main tasks that were addressed, and the main methods that were carried out.

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Notes

  1. 1.

    Although it was also suggested that this may not necessarily have a substantial influence on the generalizability of the deep learning models.

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Authors and Affiliations

  1. Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA

    P. Burlina & N. Joshi

  2. Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    N. M. Bressler

Authors
  1. P. Burlina
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  2. N. Joshi
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  3. N. M. Bressler
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Corresponding author

Correspondence to P. Burlina .

Editor information

Editors and Affiliations

  1. School of Computer Science, University of Adelaide, Adelaide, Australia

    Gustavo Carneiro

  2. Data61, Commonwealth Scientific and Industrial Research Organization, Canberra, Australia

    Shaodi You

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Burlina, P., Joshi, N., Bressler, N.M. (2019). AI-based AMD Analysis: A Review of Recent Progress. 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_25

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  • DOI: https://doi.org/10.1007/978-3-030-21074-8_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21073-1

  • Online ISBN: 978-3-030-21074-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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