A novel attention fusion network-based multiple instance learning framework to automate diagnosis of chronic gastritis with multiple indicators
10.3760/cma.j.cn112151-20210314-00204
- VernacularTitle:基于注意力机制网络的多实例学习框架实现慢性胃炎多项病理指标的自动识别
- Author:
Dan HUANG
1
;
Yi WANG
;
Qinghua YOU
;
Xin WANG
;
Jingyi ZHANG
;
Xie DING
;
Boqiang ZHANG
;
Haoyang CUI
;
Jiaxu ZHAO
;
Weiqi SHENG
Author Information
1. 复旦大学附属肿瘤医院病理科 复旦大学上海医学院肿瘤学系 复旦大学病理研究所 200032
- Keywords:
Gastritis;
Artificial intelligence;
Pattern recognition, automated;
Signal processing, computer-assisted;
Attention
- From:
Chinese Journal of Pathology
2021;50(10):1116-1121
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the performance of the attention-multiple instance learning (MIL) framework, an attention fusion network-based MIL, in the automated diagnosis of chronic gastritis with multiple indicators.Methods:A total of 1 015 biopsy cases of gastritis diagnosed in Fudan University Cancer Hospital, Shanghai, China and 115 biopsy cases of gastritis diagnosed in Shanghai Pudong Hospital, Shanghai, China were collected from January 1st to December 31st in 2018. All pathological sections were digitally converted into whole slide imaging (WSI). The WSI label was based on the corresponding pathological report, including "activity" "atrophy" and "intestinal metaplasia". The WSI were divided into a training set, a single test set, a mixed test set and an independent test set. The accuracy of automated diagnosis for the Attention-MIL model was validated in three test sets.Results:The area under receive-operator curve (AUC) values of Attention-MIL model in single test sets of 240 WSI were: activity 0.98, atrophy 0.89, and intestinal metaplasia 0.98; the average accuracy of the three indicators was 94.2%. The AUC values in mixed test sets of 117 WSI were: activity 0.95, atrophy 0.86, and intestinal metaplasia 0.94; the average accuracy of the three indicators was 88.3%. The AUC values in independent test sets of 115 WSI were: activity 0.93, atrophy 0.84, and intestinal metaplasia 0.90; the average accuracy of the three indicators was 85.5%.Conclusions:To assist in pathological diagnosis of chronic gastritis, the diagnostic accuracy of Attention-MIL model is very close to that of pathologists. Thus, it is suitable for practical application of artificial intelligence technology.
