吉林大学学报（理学版）2026，Vol.64Issue(3)：581-590,10.DOI:10.13413/j.cnki.jdxblxb.2025055
基于边界感知与特征融合的病理图像分割网络
Pathological Image Segmentation Network Based on Boundary-Aware and Feature Fusion
摘要
Abstract
Aiming at the problems of insufficient accuracy of pathological image recognition and semantic gaps in feature fusion process caused by the diversity of lesion morphology,we proposed an improved U-Net architecture that integrated Transformer and attention mechanisms.Firstly,we designed a boundary-aware module to enhance the expression of lesion edge features in pathological images,thereby improving the model's ability to perceive complex structures.Secondly,we introduced a regularized large-kernel attention module at the bottleneck layer to model long-range dependencies,and mitigated overfitting risk through a layer-wise regularization strategy.Finally,we further introduced a learnable visual center module to strengthen the complementarity between global and local features.Experimental results on the MoNuSeg and GlaS datasets show that the proposed method outperforms current mainstream models in terms of segmentation accuracy and boundary clarity.关键词
边界感知/特征融合/病理图像/图像分割/卷积神经网络/Transformer架构Key words
boundary-aware/feature fusion/pathological image/image segmentation/convolutional neural network/Transformer architecture分类
信息技术与安全科学引用本文复制引用
陈海鹏,孔鸣,张洪语,孙宝胜..基于边界感知与特征融合的病理图像分割网络[J].吉林大学学报（理学版）,2026,64(3):581-590,10.基金项目
吉林省科技发展计划重点研发项目(批准号:YDZJ202502CXJD068)和国家自然科学基金面上项目(批准号:62276112). （批准号:YDZJ202502CXJD068）
