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Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier

Crossref DOI link: https://doi.org/10.1007/s00417-018-4098-2

Published Online: 2018-08-08

Published Print: 2018-11

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Treder, Maximilian http://orcid.org/0000-0002-8533-6026
Lauermann, Jost Lennart

Eter, Nicole
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Text and Data Mining valid from 2018-08-08
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Article History

Received: 3 March 2018

Revised: 4 July 2018

Accepted: 2 August 2018

First Online: 8 August 2018

Compliance with ethical standards

:

: All authors certify that they have no affiliations with/or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements) or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

: M. Treder: Allergan, Novartis; J.L. Lauermann: Bayer, Novartis; N. Eter: Allergan, Alimera, Bausch and Lomb, Bayer, Heidelberg Engineering, Novartis, Roche.

: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.For this type of study formal consent is not required.