基于多尺度卷积和注意力机制的病理图像分割网络

曾安; 赖峻浩; 杨宝瑶; 潘丹

doi:10.12052/gdutxb.230179

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基于多尺度卷积和注意力机制的病理图像分割网络

计算机科学与技术 | 更新时间：2026-05-27

- 基于多尺度卷积和注意力机制的病理图像分割网络
- Pathology Image Segmentation Network Based on Multiscale Convolution and Attention Mechanism
- 广东工业大学学报 2025年42卷第2期页码：1-10
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 广东技术师范大学电子与信息学院,广东,广州,510665
- 作者简介：
  
  曾安(1978–)，女，教授，博士生导师，主要研究方向为图像处理、模式识别及人工智能，E-mail：zengan@gdut.edu.cn
  杨宝瑶 (1991–) ，女，副教授，研究生导师，主要研究方向为模式识别与数据挖掘、知识表示与处理、机器学习、计算机图像视频处理与多媒体技术，E-mail：ybaoyao@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61976058，92267107);广东省重点领域研发计划项目(2021B0101220006);广东省科技计划项目(2019A050510041);广东省自然科学基金资助项目(2021A1515012300);广州市科技计划项目 (202103000034，202002020090)
- DOI：10.12052/gdutxb.230179
  中图分类号： TP391;TP181
- 纸质出版：2025
- 稿件说明：
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曾安, 赖峻浩, 杨宝瑶, 等. 基于多尺度卷积和注意力机制的病理图像分割网络[J]. 广东工业大学学报, 2025, 42(2): 1–10.

Zeng An, Lai Junhao, Yang Baoyao, et al. Pathology Image Segmentation Network Based on Multiscale Convolution and Attention Mechanism[J]. 2025, 42(2): 1-10.
曾安, 赖峻浩, 杨宝瑶, 等. 基于多尺度卷积和注意力机制的病理图像分割网络[J]. 广东工业大学学报, 2025, 42(2): 1–10. DOI： 10.12052/gdutxb.230179

Zeng An, Lai Junhao, Yang Baoyao, et al. Pathology Image Segmentation Network Based on Multiscale Convolution and Attention Mechanism[J]. 2025, 42(2): 1-10. DOI： 10.12052/gdutxb.230179.

摘要

深度学习在病理图像分割中具有重要作用，然而现有深度学习方法在处理多尺度的病理图像分割任务上存在分割性能不足，泛化能力差等问题。针对上述问题，本文提出了一种基于多尺度卷积和注意力机制的病理图像分割网络。该网络通过多尺度卷积注意力模块，从不同尺度出发对特征图进行特征提取并从空间维度出发关注全局上下文关联信息，有效过滤冗余噪声信息，提升网络处理多尺度病理图像数据的泛化能力；通过多尺度特征融合模块，将不同尺度的特征进行信息融合，丰富了特征图的边缘信息和细粒度信息，改善分割效果。在GlaS、MoNuSeg和Lizard数据集上分别进行实验，所提方法的Dice指标在3个数据集上分别为91.07%、81.00%、79.87%，IoU指标为84.13%、68.22%、67.26%。实验结果表明，本文所提方法能够有效分割病理图片，提升分割准确度，为临床诊断提供可靠依据。

Abstract

Deep learning plays an essential role in the segmentation of pathological images. However

most existing deep learning methods still face challenges such as poor segmentation performance and generalization ability on multi-scale pathological image segmentation tasks. To address these issues

we propose a pathological image segmentation network based on multi-scale convolution and attention mechanisms. We design a multi-scale convolution attention module to extract different scales of features and spatially capture global contextual correlation information

effectively filtering redundant noise information and improving the network's generalization ability in handling multi-scale pathological image data. Additionally

we design a multi-scale feature fusion module to integrate features from different scales

enhancing the edge and fine-grained information in the feature maps and improving segmentation results. The experiments were performed on the GlaS

MoNuSeg and Lizard datasets

and the experimental results show that the Dice scores of the proposed method were 91.07%、81.00% and 79.87%

respectively

and the IoU scores were 84.13%、68.22% and 67.26%

respectively. This demonstrates that the proposed method can effectively segment pathology image

improve the segmentation accuracy

and provide a reliable basis for clinical diagnosis.

关键词

Keywords

references

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