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2021 | OriginalPaper | Chapter

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

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

Published in: KI 2021: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

The chapter discusses the challenge of diabetic retinopathy (DR) grading due to the complexity and variability of retinal images. It introduces a self-supervised domain adaptation method that uses vessel image reconstruction tasks to learn invariant feature representations. This approach addresses the domain shift problem in medical images, enabling more accurate and robust DR grading systems. The method is benchmarked against state-of-the-art unsupervised domain adaptation techniques and shows promising results. The chapter also highlights the potential for future research in incorporating additional lesion appearances and multimodal embeddings to further improve performance.
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Metadata
Title
Self-supervised Domain Adaptation for Diabetic Retinopathy Grading Using Vessel Image Reconstruction
Authors
Duy M. H. Nguyen
Truong T. N. Mai
Ngoc T. T. Than
Alexander Prange
Daniel Sonntag
Copyright Year
2021
Book
KI 2021: Advances in Artificial Intelligence

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

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

Copyright Year: 2021

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

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