| 基于卷积神经网络的胃癌病理诊断的应用及进展 |
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| 引用本文： | 郭昕萌,赵宏颖,石中月,王莹,金木兰.基于卷积神经网络的胃癌病理诊断的应用及进展[J].四川大学学报(医学版),2021,52(2):166-169. |
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| 作者姓名： | 郭昕萌 赵宏颖 石中月 王莹 金木兰 |
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| 作者单位： | 首都医科大学附属北京朝阳医院 病理科 （北京 100000） |
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| 基金项目： | 国家自然科学基金（No. 81972248）和北京市自然科学基金（No. 7202056）资助 |
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| 摘 要： | 由于胃癌早期隐匿性强，我国目前胃癌的早期诊断率仍较低，且主要依赖于病理专家人工诊断。近年来，随着人工智能和数字病理学的快速发展，以卷积神经网络为核心技术的人工智能辅助病理诊断技术不仅有望提高胃癌的诊断效率，对于提高我国胃癌的早诊早治水平，改善其高发病率、高死亡率现状，也具有重要意义。本述评旨在针对基于深度学习卷积神经网络在胃癌病理诊断中的应用及进展进行归纳，着眼于利用卷积神经网络识别癌变区域、组织学分类、判断肿瘤浸润深度、指导患者用药，总结其现存问题及未来发展。我们认为，以下局限性及困难亟待解决:病理切片数字化耗时、耗存储；数据的标注及数据的质量控制问题；数据整合问题；法律责任界定问题。未来随着人工智能与病理学的融合，基于人工智能的病理诊断将广泛应用于临床工作中，如将深度学习的卷积神经网络应用于胃早癌的筛查工作，可大为减轻病理医师的工作负担；而患者个人进行胃癌自查自检，也是人工智能辅助诊断胃癌可能实现突破的方向。
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| 关 键 词： | 卷积神经网络 胃癌 数字病理 诊断 治疗 |
| 收稿时间： | 2020-11-04 |
Application and Progress of Convolutional Neural Network-based Pathological Diagnosis of Gastric Cancer |
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| GUO Xin-meng,ZHAO Hong-ying,SHI Zhong-yue,WANG Ying,JIN Mu-lan.Application and Progress of Convolutional Neural Network-based Pathological Diagnosis of Gastric Cancer[J].Journal of West China University of Medical Sciences,2021,52(2):166-169. |
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| Authors: | GUO Xin-meng ZHAO Hong-ying SHI Zhong-yue WANG Ying JIN Mu-lan |
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| Affiliation: | Department of Pathology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100000, China |
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| Abstract: | 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. |
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