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TSSP‐UNet: A Two‐Stage Weakly Supervised Pathological Image Segmentation With Point Annotations

Shaoqiang Wang Orcid Logo, Guiling Shi, Yuchen Wang, Qiang Li, Yawu Zhao, Cheng Cheng Orcid Logo

IET Systems Biology, Volume: 20, Issue: 1

Swansea University Author: Cheng Cheng Orcid Logo

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DOI (Published version): 10.1049/syb2.70055

Abstract

Deep convolutional neural networks have demonstrated remarkable effectiveness in image segmentation. However, segmentation becomes challenging when training on images with complex instances. Moreover, obtaining annotations for high-precision data is also difficult. Weakly supervised learning can add...

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Published in: IET Systems Biology
ISSN: 1751-8849 1751-8857
Published: Institution of Engineering and Technology (IET) 2026
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URI: https://cronfa.swan.ac.uk/Record/cronfa71290
first_indexed 2026-01-21T14:58:44Z
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spelling 2026-04-17T15:16:04.8979181 v2 71290 2026-01-21 TSSP‐UNet: A Two‐Stage Weakly Supervised Pathological Image Segmentation With Point Annotations 11ddf61c123b99e59b00fa1479367582 0000-0003-0371-9646 Cheng Cheng Cheng Cheng true false 2026-01-21 MACS Deep convolutional neural networks have demonstrated remarkable effectiveness in image segmentation. However, segmentation becomes challenging when training on images with complex instances. Moreover, obtaining annotations for high-precision data is also difficult. Weakly supervised learning can address this issue by using nonspecialised annotations or supervised information from segmentation algorithms. In this study, we proposed TSSP-UNet: a two-stage weakly supervised segmentation approach. In the first stage, we trained a segmentation network augmented with constraint and attention mechanisms. These mechanisms are designed to operate on boundaries and superpixels generated from pseudo-labels. For the attention network, two pseudo-labels were used with a binary mask to add contour information to the segmentation process. Furthermore, a feature aggregation segmentation network was applied to the prominent foreground area in the image by incrementally adding elements. In the second stage, a refined confident learning algorithm improved the pseudo-labels at the pixel level and then TSSP-UNet was retrained using the modified superpixel labels. Testing on the MoNuSeg and TNBC datasets demonstrates that the approach performs well in the weakly supervised cell nucleus segmentation task compared with baseline methods. Journal Article IET Systems Biology 20 1 Institution of Engineering and Technology (IET) 1751-8849 1751-8857 Nucleus segmentation; Constraint networks; Segmentation networks; Attention networks; Confident learning 1 1 2026 2026-01-01 10.1049/syb2.70055 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University SU Library paid the OA fee (TA Institutional Deal) This work was funded by by UKRI Grant EP/W020408/1 and Grant RS718 through Doctoral Training Centre at Swansea University. 2026-04-17T15:16:04.8979181 2026-01-21T14:50:35.0358193 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Shaoqiang Wang 0000-0002-7539-5970 1 Guiling Shi 2 Yuchen Wang 3 Qiang Li 4 Yawu Zhao 5 Cheng Cheng 0000-0003-0371-9646 6 71290__36525__2a56dacca06f4d2b8178e9f4856e8105.pdf 71290.VoR.pdf 2026-04-17T15:13:21.4764005 Output 4945740 application/pdf Version of Record true © 2026 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/
title TSSP‐UNet: A Two‐Stage Weakly Supervised Pathological Image Segmentation With Point Annotations
spellingShingle TSSP‐UNet: A Two‐Stage Weakly Supervised Pathological Image Segmentation With Point Annotations
Cheng Cheng
title_short TSSP‐UNet: A Two‐Stage Weakly Supervised Pathological Image Segmentation With Point Annotations
title_full TSSP‐UNet: A Two‐Stage Weakly Supervised Pathological Image Segmentation With Point Annotations
title_fullStr TSSP‐UNet: A Two‐Stage Weakly Supervised Pathological Image Segmentation With Point Annotations
title_full_unstemmed TSSP‐UNet: A Two‐Stage Weakly Supervised Pathological Image Segmentation With Point Annotations
title_sort TSSP‐UNet: A Two‐Stage Weakly Supervised Pathological Image Segmentation With Point Annotations
author_id_str_mv 11ddf61c123b99e59b00fa1479367582
author_id_fullname_str_mv 11ddf61c123b99e59b00fa1479367582_***_Cheng Cheng
author Cheng Cheng
author2 Shaoqiang Wang
Guiling Shi
Yuchen Wang
Qiang Li
Yawu Zhao
Cheng Cheng
format Journal article
container_title IET Systems Biology
container_volume 20
container_issue 1
publishDate 2026
institution Swansea University
issn 1751-8849
1751-8857
doi_str_mv 10.1049/syb2.70055
publisher Institution of Engineering and Technology (IET)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Deep convolutional neural networks have demonstrated remarkable effectiveness in image segmentation. However, segmentation becomes challenging when training on images with complex instances. Moreover, obtaining annotations for high-precision data is also difficult. Weakly supervised learning can address this issue by using nonspecialised annotations or supervised information from segmentation algorithms. In this study, we proposed TSSP-UNet: a two-stage weakly supervised segmentation approach. In the first stage, we trained a segmentation network augmented with constraint and attention mechanisms. These mechanisms are designed to operate on boundaries and superpixels generated from pseudo-labels. For the attention network, two pseudo-labels were used with a binary mask to add contour information to the segmentation process. Furthermore, a feature aggregation segmentation network was applied to the prominent foreground area in the image by incrementally adding elements. In the second stage, a refined confident learning algorithm improved the pseudo-labels at the pixel level and then TSSP-UNet was retrained using the modified superpixel labels. Testing on the MoNuSeg and TNBC datasets demonstrates that the approach performs well in the weakly supervised cell nucleus segmentation task compared with baseline methods.
published_date 2026-01-01T05:56:09Z
_version_ 1862782859043930112
score 11.102276

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