Glandular Tissue Segmentation Based on EMA-Swin UNet Model

Glandular Tissue Segmentation Based on EMA-Swin UNet Model | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 January 2025 V1 Latest version Share on Glandular Tissue Segmentation Based on EMA-Swin UNet Model Authors : Hongnan Cheng 0009-0002-8638-2937 , Chaozhi Yu 0009-0006-0087-3161 , Yan Zhang , Boyu Li , Wenxiang Huang , and Chenguang Zhang 0009-0002-2607-2128 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.173641600.01007870/v1 566 views 307 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The accurate gland segmentation from digitized H&E (hematoxylin and eosin) histology images with a wide range of histologic grades of cancer is quite challenging. In recent years, several fully convolutional network methods have been proposed, with UNet being the most classic. The UNet model, with its symmetric structure, has shown excellent performance in gland segmentation tasks. However, the locality of convolution operations in UNet also limits its ability to capture global dependencies. To address this limitation, this paper proposes a novel deep glandular tissue image segmentation network based on Swin UNet, termed EMA-Swin UNet. This network replaces CNN modules with Swin Transformer modules to capture both local and global representations. Additionally, the EMA-Swin UNet incorporates an Efficient Multi-scale Attention (EMA) module to enhance multi-scale feature extraction for glandular tissues of various sizes by capturing global dynamic features and long-range smooth features from the encoder outputs. By integrating edge-detection pooling, we enhanced the refinement of prediction maps produced by the EMA-Swin UNet. Moreover, we standardized the staining across both the ClaS dataset and the six-grade tumor differentiation dataset from EBHI-Seg using Reinhard normalization. The final segmentation results are compared with those of classical gland segmentation algorithms on the ClaS and EBHI-Seg datasets, demonstrating the effectiveness of our proposed method. Particularly, on the GlaS dataset, the mDice reached 0.894. Supplementary Material File (glandular tissue segmentation based on ema-swin unet model.pdf) Download 2.23 MB Information & Authors Information Version history V1 Version 1 09 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords glandular segmentation swin transformer unet Authors Affiliations Hongnan Cheng 0009-0002-8638-2937 Hainan University View all articles by this author Chaozhi Yu 0009-0006-0087-3161 Hainan University View all articles by this author Yan Zhang Hainan University View all articles by this author Boyu Li Hainan University View all articles by this author Wenxiang Huang Hainan University View all articles by this author Chenguang Zhang 0009-0002-2607-2128 [email protected] Hainan University View all articles by this author Metrics & Citations Metrics Article Usage 566 views 307 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Hongnan Cheng, Chaozhi Yu, Yan Zhang, et al. Glandular Tissue Segmentation Based on EMA-Swin UNet Model. Authorea . 09 January 2025. DOI: https://doi.org/10.22541/au.173641600.01007870/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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K., Lalithamani N., Adeyemi Abel Ajibesin, Enhanced colorectal gland segmentation through multi-scale attention and contextual feature fusion, Scientific Reports, 16 , 1, (2026). https://doi.org/10.1038/s41598-025-34548-5 Crossref Yudi Xu, Chaozhi Yu, Hongnan Cheng, Yulai Wu, Enhancing Stock Price Prediction with GLTCN: A Hybrid Model for Complex Market Dynamics, Proceedings of the 9th International Conference on Electronic Information Technology and Computer Engineering, (695-700), (2025). https://doi.org/10.1145/3766671.3766792 Crossref Loading... View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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