Evaluating the robustness of vision transformers in retinal imaging: A preliminary study
File(s)
Author(s)
Mabo, Michael
Fetit, Ahmed
Type
Conference Paper
Abstract
In this work, we present a preliminary study that investigates the robustness of vision transformers (ViTs) compared to convolutional neural networks (CNNs) for the classification of referable diabetic retinopathy, under synthetic noise perturbations. We initially observed that ViTs outperformed CNNs on uncorrupted data, aligning with their known capabilities in capturing complex patterns. However, our preliminary results indicate that the robustness of ViTs diminished as the levels of synthetic noise within the images increased, and we did not observe improved robustness when comparing the ViTs to the
baseline CNNs.
baseline CNNs.
Date Issued
2025-07-15
Date Acceptance
2025-06-05
Citation
Frontiers Abstract Book, 2025
ISBN
978-2-8325-5137-0
Publisher
Frontiers Media SA
Journal / Book Title
Frontiers Abstract Book
Copyright Statement
© Copyright in the individual abstracts is owned by the author of each abstract or their employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (creativecommons.org/licenses/by/4.0/ ) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.
License URL
Source
29th UK Conference on Medical Image Understanding and Analysis
Publication Status
Published
Start Date
2025-07-15
Finish Date
2025-07-17
Coverage Spatial
Leeds, UK