Journal article 337 views
TSSP‐UNet: A Two‐Stage Weakly Supervised Pathological Image Segmentation With Point Annotations
,
Guiling Shi,
Yuchen Wang,
Qiang Li,
Yawu Zhao,
Cheng Cheng
IET Systems Biology, Volume: 20, Issue: 1
Swansea University Author:
Cheng Cheng
-
PDF | Version of Record
© 2026 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License.
Download (4.72MB)
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...
| Published in: | IET Systems Biology |
|---|---|
| ISSN: | 1751-8849 1751-8857 |
| Published: |
Institution of Engineering and Technology (IET)
2026
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa71290 |
| 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 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. |
|---|---|
| Keywords: |
Nucleus segmentation; Constraint networks; Segmentation networks; Attention networks; Confident learning |
| College: |
Faculty of Science and Engineering |
| Funders: |
This work was funded by by UKRI Grant EP/W020408/1 and Grant RS718 through Doctoral Training Centre at Swansea University. |
| Issue: |
1 |