临床与实验病理学杂志2026，Vol.42Issue(8)：988-995,8.DOI:10.13315/j.cnki.cjcep.2026.08.002
基于无监督-监督融合深度学习模型在慢性萎缩性胃炎病理诊断中的应用研究
Application of unsupervised-supervised fusion deep learning model in pathological diagnosis of chronic atrophic gastritis
摘要
Abstract
Objective To introduce an unsupervised-supervised fusion deep learning model to assist pathologists in grading the severity of gastric mucosal atrophy and intestinal metaplasia in gastroscopic biopsy specimens of chronic atrophic gastritis(CAG).Methods A total of 455 gastroscopic biopsy specimens diagnosed as CAG within one year were collected from Jiangsu Provincial Hospital of Chinese Medicine,and all specimens were digitized into whole slide images(WSIs).An unsupervised-supervised fusion deep learning framework was employed.WSIs from 155 biopsy specimens were utilized for model training,and the remaining 300 WSIs were adopted for model testing.Results For the grading of gastric mucosal atrophy,the model achieved an overall accuracy of 96.67%,weighted average sensitiv-ity of 96.67%,weighted average specificity of 98.88%,with a Kappa consistency coefficient of 0.95.For the grading of intestinal metaplasia,the overall accuracy,weighted average sensitivity and weighted average specificity reached 98.33%,98.33%,and 98.89%,respectively,with a Kappa consistency coefficient of 0.98.Conclusion The unsupervised-supervised fusion deep learning model can efficiently and accurately assist pathologists in grading gastric mucosal atrophy and intestinal metaplasia in CAG specimens,and exhibits excellent diagnostic consistency with manual pathological evaluation.关键词
慢性萎缩性胃炎/胃镜活检病理/无监督-监督融合/深度学习模型Key words
chronic atrophic gastritis/gastroscopic biopsy pathology/unsupervised-supervised fusion/deep learning model分类
医药卫生引用本文复制引用
王露,栗源,张逸,章宜芬..基于无监督-监督融合深度学习模型在慢性萎缩性胃炎病理诊断中的应用研究[J].临床与实验病理学杂志,2026,42(8):988-995,8.基金项目
江苏省中医院院级课题[(2023)22号-Y22073] Jiangsu Provincial Hospital of Chinese Medicine Hospital-level Research Project[(2023)No.22-Y22073] （2023）
