Deep learning in digital pathology image analysis: a survey

2020年第14卷第4期

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《医学前沿（英文）》 >> 2020年第14卷第4期 doi: 10.1007/s11684-020-0782-9

Deep learning in digital pathology image analysis: a survey

. School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China;.. Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China;.. Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China;.. Microsoft Research Asia, Beijing 100080, China;.. Department of Pathology, School of Medicine, Zhejiang University, Hangzhou 310007, China

收稿日期： 2020-06-28 录用日期： 2020-07-27 发布日期： 2020-07-27

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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.

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pathology ; deep learning ; segmentation ; detection ; classification