Gland segmentation in colorectal pathological image using dual-branch network based on weakly supervised learning
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.
NETL
NSTL
万方数据
