Bayer, S, Zhong, X, Fu, W et al. (2 more authors) (2020) Imitation Learning Network for Fundus Image Registration Using a Divide-And-Conquer Approach. In: Informatik aktuell. Bildverarbeitung für die Medizin, 15-17 Mar 2020, Berlin, Germany. Springer Vieweg, Wiesbaden , pp. 301-306. ISBN 9783658292669
Abstract
Comparison of microvascular circulation on fundoscopic images is a non-invasive clinical indication for the diagnosis and monitoring of diseases, such as diabetes and hypertensions. The differences between intra-patient images can be assessed quantitatively by registering serial acquisitions. Due to the variability of the images (i.e. contrast, luminosity) and the anatomical changes of the retina, the registration of fundus images remains a challenging task. Recently, several deep learning approaches have been proposed to register fundus images in an end-to-end fashion, achieving remarkable results. However, the results are diffcult to interpret and analyze. In this work, we propose an imitation learning framework for the registration of 2D color funduscopic images for a wide range of applications such as disease monitoring, image stitching and super-resolution. We follow a divide-and-conquer approach to improve the interpretability of the proposed network, and analyze both the influence of the input image and the hyperparameters on the registration result. The results show that the proposed registration network reduces the initial target registration error up to 95%
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020. This is an author produced version of a conference paper published in Informatik aktuell. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Jul 2021 13:32 |
Last Modified: | 27 Jul 2021 07:50 |
Status: | Published |
Publisher: | Springer Vieweg, Wiesbaden |
Identification Number: | 10.1007/978-3-658-29267-6_67 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176403 |