Journal Article
. 2020 Aug; 11(1):4294.
doi: 10.1038/s41467-020-18147-8.

Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning

Zhigang Song 1 Shuangmei Zou 2 Weixun Zhou 3 Yong Huang 1 Liwei Shao 1 Jing Yuan 1 Xiangnan Gou 1 Wei Jin 1 Zhanbo Wang 1 Xin Chen 1 Xiaohui Ding 1 Jinhong Liu 1 Chunkai Yu 4 Calvin Ku 5 Cancheng Liu 5 Zhuo Sun 5 Gang Xu 5 Yuefeng Wang 5 Xiaoqing Zhang 5 Dandan Wang 6 Shuhao Wang 7 Wei Xu 8 Richard C Davis 9 Huaiyin Shi 10 
  • PMID: 32855423
  •     35 References
  •     16 citations


The early detection and accurate histopathological diagnosis of gastric cancer increase the chances of successful treatment. The worldwide shortage of pathologists offers a unique opportunity for the use of artificial intelligence assistance systems to alleviate the workload and increase diagnostic accuracy. Here, we report a clinically applicable system developed at the Chinese PLA General Hospital, China, using a deep convolutional neural network trained with 2,123 pixel-level annotated H&E-stained whole slide images. The model achieves a sensitivity near 100% and an average specificity of 80.6% on a real-world test dataset with 3,212 whole slide images digitalized by three scanners. We show that the system could aid pathologists in improving diagnostic accuracy and preventing misdiagnoses. Moreover, we demonstrate that our system performs robustly with 1,582 whole slide images from two other medical centres. Our study suggests the feasibility and benefits of using histopathological artificial intelligence assistance systems in routine practice scenarios.

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