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. 2023 Oct 18;13:17759. doi: 10.1038/s41598-023-44782-4

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© The Author(s) 2023

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/.

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Segmentation network used in this work. (a) UPerNet is based on Feature Pyramid Network that extract image features at different scales. (b) Twins interleaves locally grouped attention (LSA) with global sub-sampled attention (GSA) during feature extraction.