基于深度学习和组织病理图像的癌症分类研究进展  

Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images

作　　者：颜锐 陈丽萌[3] 李锦涛[1] 任菲[1] YAN Rui;CHEN Limeng;LI Jintao;REN Fei(Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China;Department of Nephrology,Peking Union Medical College Hospital,Chinese Academy of Medical Sciences&Peking Union Medical College,Beijing 100730,China)

机构地区：[1]中国科学院计算技术研究所,北京100190 [2]中国科学院大学,北京100049 [3]中国医学科学院,北京协和医学院,北京协和医院肾内科,北京100730

出　　处：《协和医学杂志》2021年第5期742-748,共7页Medical Journal of Peking Union Medical College Hospital

基　　金：国家自然科学基金(82072939)。

摘　　要：癌症的精确分类直接关系到患者治疗方案的选择和预后。病理诊断是癌症诊断的金标准,病理图像的数字化和深度学习的突破性进展使得计算机辅助癌症诊断和预后预测成为可能。本文通过简述病理图像分类常用的4种深度学习方法,总结基于深度学习和组织病理图像的癌症分类最新研究进展,指出该领域研究中普遍存在的问题与挑战,并对未来可能的发展方向进行展望。Accurate classification of cancer is directly related to the choice of treatment options and prognosis.Pathological diagnosis is the gold standard for cancer diagnosis.The digitalization of pathological images and breakthroughs in deep learning have made computer-aided diagnosis and prediction about prognosis possible.In this paper,we first briefly describe four deep learning methods commonly used in this field,and then review the latest research progress in cancer classification based on deep learning and histopathological images.Finally,the general problems in this field are summarized,and the possible development direction in the future is suggested.

关 键 词：病理图像 深度学习 癌症分类 癌症分级 计算机辅助诊断 

分 类 号：R73[医药卫生—肿瘤] R445[医药卫生—临床医学]