电子科技2025，Vol.38Issue(11)：18-24,7.DOI:10.16180/j.cnki.issn1007-7820.2025.11.003
基于深度特征融合的内窥镜图像分割网络
Endoscopic Image Segmentation Network Based on Deep Feature Fusion
摘要
Abstract
Gastroscope examination is a medical diagnostic technique for early diagnosis and preventative treat-ment of gastric cancer through detection and resection of lesion mucosa.Deep learning method has great application po-tential in gastroscopy.There is currently a lack of publicly available early gastric cancer data sets for researchers to use,and existing endoscopic segmentation methods cannot efficiently identify gastroscopic lesions.To solve this prob-lem,a new dataset Colonoscopy is proposed in this study,including the segmentation task of early gastric cancer lesions and the multi-classification task from healthy to early cancer stage.An endoscopic image segmentation architecture based on deep feature fusion is also proposed.A pre-trained MTE(Mixed Transformer Encoder)is used as the main work,and a deep fusion feature pyramid decoder is used to achieve accurate lesion segmentation.The proposed method has better performance in Colonoscopy data set,and has better generalization ability in two large colonoscopy data sets including Kvasir-Seg and CVC-ClinicDB.关键词
胃早癌/内窥镜图像分割/病理诊断/深度学习/Transformer/特征融合/一致性/分割数据集Key words
early gastric cancer/endoscopic image segmentation/pathological diagnosis/deep learning/Trans-former/feature fusion/consistency/segmentation data set分类
计算机与自动化引用本文复制引用
向振凯,王永雄,张佳鹏..基于深度特征融合的内窥镜图像分割网络[J].电子科技,2025,38(11):18-24,7.基金项目
上海市自然科学基金(22ZR1443700) Natural Science Foundation of Shanghai(22ZR1443700) （22ZR1443700）
