Deep learning in digital pathology image analysis: a survey

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...

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Bibliographic Details
Published in:Frontiers of medicine 2020-08, Vol.14 (4), p.470-487
Main Authors: Deng, Shujian, Zhang, Xin, Yan, Wen, Chang, Eric I-Chao, Fan, Yubo, Lai, Maode, Xu, Yan
Format: Article
Language:English
Subjects:
classification
Deep Learning
detection
Histopathology
Machine Learning
Medical imaging
Medicine
Medicine & Public Health
Pathology
Review
segmentation
Surveys and Questionnaires
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Summary: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.
ISSN:2095-0217
2095-0225
DOI:10.1007/s11684-020-0782-9