[A novel attention fusion network-based multiple instance learning framework to automate diagnosis of chronic gastritis with multiple indicators].

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.