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. 2024 Apr 23;10:36. doi: 10.1186/s40942-024-00554-4

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — Automated segmentation of soft exudates (SE), hard exudates (EX), hemorrhage (HE), and microaneurysms (MA) using multiple U-Net architectures. Reprinted with permission from Xu et al. Xu Y, Zhou Z, Li X, Zhang N, Zhang M, Wei P. FFU-Net: Feature Fusion U-Net for Lesion Segmentation of Diabetic Retinopathy. Biomed Res Int. 2021 Jan 2;2021:6644071 under Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/legalcode)