LGDUnet: A dual-branch network for weakly supervised gland segmentation in H&E stained histopathological images.
Journal:
Biomedical physics & engineering express
Published Date:
Jan 16, 2026
Abstract
Glandular segmentation holds significant importance in the pathological analysis of hematoxylin and eosin (H&E) stained images, particularly in the diagnosis and treatment of breast and colorectal cancers. Accurate segmentation of glandular structures provides critical support for lesion detection and pathological assessment. Traditional fully supervised learning methods typically require a substantial amount of labeled data, which is often challenging to obtain in the medical field. To address this issue, this study proposes a novel weakly supervised segmentation method that integrates pseudo-labels and consistency contrast loss with random noise, achieving results comparable to several state-of-the-art architectures trained under full supervision. The proposed network model, LGDUnet, features both global and local branch structures. The global branch focuses on the overall structure of the glands, while the local branch emphasizes the capture of fine edge features. The encoder utilizes a combination of ResHorBlock and WSAB to enhance feature extraction capabilities through higher-order interactions and weighted spatial attention at various levels. The decoder employs the Dual Self-Attention Transpose (DST) structure, which enhances reconstruction accuracy through a dual self-attention mechanism. Skip connections are implemented using the Dual Attention Transformer (DAT) for encoder feature transformation, further improving the efficacy of feature propagation.We conducted comprehensive comparative and ablation experiments on the benchmark dataset GlaS from the MICCAI 2015 Challenge, our self-constructed breast cancer dataset Tubule of Breast Cancer (TBC), and the Colorectal Cancer Gland Dataset (CGD). The experimental results demonstrate that LGDUnet exhibits superior performance in glandular segmentation tasks, validating the effectiveness of this method within the weakly supervised learning framework.
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