Abstract:

Gastric cancer is the 3rd leading cause of cancer death worldwide, and the early detection of gastric cancer will play a crucial role in the later treatment of patients with gastric cancer. With the development of artificial intelligence, machine learning models in the field of computer vision can be utilized to assist in the early detection of gastric cancer, and some studies have found that the screening rate of some computer-assisted diagnostic models is close to or even higher than that of doctors. The early detection of gastric cancer using computer-assisted diagnosis can reduce the later treatment costs for patients with gastric cancer. This paper reports the current state of research on the assisted diagnosis of early gastric cancer under gastroscopy based on machine learning, introduces the clinical diagnostic modalities of early gastric cancer under gastroscopy and puts forward a diagnostic technology route for the computer-assisted diagnosis of the disease, analyzes the research characteristics of different diagnostic technology routes, and provides different entry points for computer-assisted diagnosis of early gastric cancer. This paper summarizes the models of machine learning, deep learning, target detection for early gastric cancer detection, and discusses the problems and challenges of their application to computer-assisted diagnosis.

Key words: early gastric cancer, machine learning, deep learning, object detection

摘要：

胃癌是全世界癌症死亡的第三大主要原因，胃癌的早期检测会对胃癌患者的后期治疗起到至关重要的作用。随着人工智能的发展，可以利用计算机视觉领域的机器学习模型辅助检测早期胃癌，有研究发现一些计算机辅助诊断模型的筛查率接近甚至高于医生。利用计算机辅助诊断可以及早发现胃癌以减少胃癌患者的后期治疗成本。报告了基于机器学习在胃镜下早期胃癌辅助诊断的研究现状，介绍了胃镜下早期胃癌的临床诊断方式，并基于此提出了计算机辅助诊断该疾病的技术路线，分析了不同诊断技术路线的研究特点，为计算机辅助诊断早期胃癌提供不同的切入点。总结了用于早期胃癌检测的机器学习、深度学习、目标检测模型，讨论了其应用于计算机辅助诊断的问题及挑战。

关键词: 早期胃癌, 机器学习, 深度学习, 目标检测