Application and Progress of Convolutional Neural Network-based Pathological Diagnosis of Gastric Cancer

The incidence of gastric cancer is the highest among all kinds of malignant tumors in China. Because gastric cancer is very hard to identify in its early stage, the early diagnosis rate of gastric cancer in China is relatively low. At present, the pathological diagnosis of gastric cancer mainly depe...

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Bibliographic Details
Published in:Sichuan da xue xue bao. Journal of Sichuan University. Yi xue ban 2021-03, Vol.52 (2), p.166-169
Main Authors: Guo, Xin-Meng, Zhao, Hong-Ying, Shi, Zhong-Yue, Wang, Ying, Jin, Mu-Lan
Format: Article
Language:Chinese
Subjects:
Artificial Intelligence
China - epidemiology
Humans
Neural Networks, Computer
Stomach Neoplasms - diagnosis
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Summary:The incidence of gastric cancer is the highest among all kinds of malignant tumors in China. Because gastric cancer is very hard to identify in its early stage, the early diagnosis rate of gastric cancer in China is relatively low. At present, the pathological diagnosis of gastric cancer mainly depends on the diagnosis of pathologists. However, the gradual improvement of people's living standards and the growing demand for medical and health care have exacerbated the shortage of medical resources, which has become a even more serious problem. Therefore, there is an urgent need for new technologies to help deal with this challenge. In recent years, with the rapid development of artificial intelligence (AI) and digital pathology, AI-aided pathological diagnosis based on convolutional neural network (CNN) as the core technology is showing promises for improving the diagnostic efficiency of gastric cancer. It is also of great significance for the early diagnosis and treatment of the disease and the reduction of its high incidence and mortality. We herein summarize the application and progress of deep-learning CNN in pathological diagnosis of gastric cancer, as well as the existing problems and prospects of future development.
ISSN:1672-173X
DOI:10.12182/20210360501