Gland segmentation in colorectal pathological image using dual-branch network based on weakly supervised learning
10.3969/j.issn.1005-202X.2024.09.007
- VernacularTitle:基于弱监督学习的双分支结直肠病理图像腺体分割
- Author:
Zicheng LI
1
;
Wei JIA
;
Xuefen ZHAO
;
Hongjuan GAO
Author Information
1. 宁夏大学信息工程学院,宁夏银川 750021
- Keywords:
weakly supervised learning;
colorectal pathological image;
gland segmentation;
pseudo-label;
class activation map
- From:
Chinese Journal of Medical Physics
2024;41(9):1104-1114
- CountryChina
- Language:Chinese
-
Abstract:
To address the issue that the existing weakly supervised segmentation methods have difficulties in obtaining fine-grained glandular features from colorectal pathological images,leading to the inability to generate high-quality pseudo-labels and compromising the gland segmentation performance,a dual-branch network based on weakly supervised learning is proposed for gland segmentation in colorectal pathological image.The patch-level colorectal pathological images are input into the first branch network,where the interaction and fusion of local and global features of patch-level images are achieved through the feature interaction module and affinity attention fusion module,and fine-grained glandular features are obtained.Subsequently,image-level colorectal pathological images are input into the second branch network,where the gland locations are located using the partial class activation attention module,and coarse-grained class activation maps are obtained.Finally,high-quality pseudo-labels are derived from the fine-grained glandular features and coarse-grained class activation maps,and gland segmentation is realized in the segmentation network through the cross-scale connected spatial perception module.The tests on two colorectal pathological image datasets(GlaS and CRAG)reveal that the proposed method is superior to other segmentation methods in segmentation performance,confirming its effectiveness.
