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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

Intermediate Goals in Deep Learning for Retinal Image Analysis

verfasst von : Gilbert Lim, Wynne Hsu, Mong Li Lee

Erschienen in: Computer Vision – ACCV 2018 Workshops

Verlag: Springer International Publishing

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Abstract

End-to-end deep learning has been demonstrated to exhibit human-level performance in many retinal image analysis tasks. However, such models’ generalizability to data from new sources may be less than optimal. We highlight some benefits of introducing intermediate goals in deep learning-based models.

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Vorheriges Kapitel Recent Developments of Retinal Image Analysis in Alzheimer’s Disease and Potential AI Applications
Nächstes Kapitel Enhanced Detection of Referable Diabetic Retinopathy via DCNNs and Transfer Learning
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Metadaten
Titel
Intermediate Goals in Deep Learning for Retinal Image Analysis
verfasst von
Gilbert Lim
Wynne Hsu
Mong Li Lee
Copyright-Jahr
2019
Buch
Computer Vision – ACCV 2018 Workshops

Print ISBN: 978-3-030-21073-1

Electronic ISBN: 978-3-030-21074-8

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-21074-8_22

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