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2021 | OriginalPaper | Buchkapitel

Self-supervised Domain Adaptation for Diabetic Retinopathy Grading Using Vessel Image Reconstruction

verfasst von : Duy M. H. Nguyen, Truong T. N. Mai, Ngoc T. T. Than, Alexander Prange, Daniel Sonntag

Erschienen in: KI 2021: Advances in Artificial Intelligence

Verlag: Springer International Publishing

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Abstract

This paper investigates the problem of domain adaptation for diabetic retinopathy (DR) grading. We learn invariant target-domain features by defining a novel self-supervised task based on retinal vessel image reconstructions, inspired by medical domain knowledge. Then, a benchmark of current state-of-the-art unsupervised domain adaptation methods on the DR problem is provided. It can be shown that our approach outperforms existing domain adaption strategies. Furthermore, when utilizing entire training data in the target domain, we are able to compete with several state-of-the-art approaches in final classification accuracy just by applying standard network architectures and using image-level labels.

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Metadaten
Titel
Self-supervised Domain Adaptation for Diabetic Retinopathy Grading Using Vessel Image Reconstruction
verfasst von
Duy M. H. Nguyen
Truong T. N. Mai
Ngoc T. T. Than
Alexander Prange
Daniel Sonntag
Copyright-Jahr
2021
Buch
KI 2021: Advances in Artificial Intelligence

Print ISBN: 978-3-030-87625-8

Electronic ISBN: 978-3-030-87626-5

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-87626-5_26

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