Contrastive Learning for Generating Optical Coherence Tomography Images of the Retina
Kaplan, Sinan; Lensu, Lasse (2022-09-21)
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Sisältö avataan julkiseksi: 22.09.2023
Sisältö avataan julkiseksi: 22.09.2023
Post-print / Final draft
Kaplan, Sinan
Lensu, Lasse
21.09.2022
13570
112-121
Springer, Cham
Lecture Notes in Computer Science
School of Engineering Science
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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2022102162694
https://urn.fi/URN:NBN:fi-fe2022102162694
Tiivistelmä
As a self-supervised learning technique, contrastive learning is an effective way to learn rich and discriminative representations from data. In this study, we propose a variational autoencoder (VAE) based approach to apply contrastive learning for the generation of optical coherence tomography (OCT) images of the retina. The approach first learns embedding representation from data by contrastive learning. Secondly, the learnt embeddings are used to synthesize disease-specific OCT images using VAEs. Our results reveal that the diseases are separated well in the embedding space and the proposed approach is able to generate high-quality images with fine-grained spatial details. The source code of the experiments in this paper can be found on Github (https://github.com/kaplansinan/OCTRetImageGen_CLcVAE).
Lähdeviite
Kaplan, S., Lensu, L. (2022). Contrastive Learning for Generating Optical Coherence Tomography Images of the Retina. In: Zhao, C., Svoboda, D., Wolterink, J.M., Escobar, M. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2022. Lecture Notes in Computer Science, vol 13570. Springer, Cham. https://doi.org/10.1007/978-3-031-16980-9_11
Alkuperäinen verkko-osoite
https://link.springer.com/chapter/10.1007/978-3-031-16980-9_11Kokoelmat
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