Deep learning in digital pathology image analysis:a survey  

作　　者：Shujian Deng Xin Zhang Wen Yan Eric I-Chao Chang Yubo Fan Maode Lai Yan Xu 

机构地区：[1]School of Biological Science and Medical Engineering,Beihang University,Beijing 100191,China [2]Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and State Key Laboratory of Software Development Environment,Beihang University,Beijing 100191,China [3]Beijing Advanced Innovation Center for Biomedical Engineering,Beihang University,Beijing 100191,China [4]Microsoft Research Asia,Beijing 100080,China [5]Department of Pathology,School of Medicine,Zhejiang University,Hangzhou 310007,China

出　　处：《Frontiers of Medicine》2020年第4期470-487,共18页医学前沿（英文版）

基　　金：This work was supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China(No.2017YFC0110903);Microsoft Research under the eHealth program;the National Natural Science Foundation of China(No.81771910);the Beijing Natural Science Foundation in China(No.4152033);the Technology and Innovation Commission of Shenzhen in China(No.shenfagai2016-627);the Beijing Young Talent Project in China,the Fundamental Research Funds for the Central Universities of China(No.SKLSDE-2017ZX-08)from the State Key Laboratory of Software Development Environment in Beihang University in China,and the 111 Project in China(No.B13003).

摘　　要：deep learning(DL)has achieved state-of-the-art performance in many digital pathology analysis tasks.Traditional methods usually require hand-crafted domain-specific features,and DL methods can learn representations without manually designed features.In terms of feature extraction,DL approaches are less labor intensive compared with conventional machine learning methods.In this paper,we comprehensively summarize recent DL-based image analysis studies in histopathology,including different tasks(e.g.,classification,semantic segmentation,detection,and instance segmentation)and various applications(e.g.,stain normalization,cell/gland/region structure analysis).DL methods can provide consistent and accurate outcomes.DL is a promising tool to assist pathologists in clinical diagnosis.

关 键 词：PATHOLOGY deep learning SEGMENTATION DETECTION CLASSIFICATION 

分 类 号：R445[医药卫生—影像医学与核医学] TP39[医药卫生—诊断学]