Journal
FRONTIERS OF MEDICINE
Volume 14, Issue 4, Pages 470-487Publisher
SPRINGER
DOI: 10.1007/s11684-020-0782-9
Keywords
pathology; deep learning; segmentation; detection; classification
Categories
Funding
- National Science and Technology Major Project of the Ministry of Science and Technology of China [2017YFC0110903]
- Microsoft Research under the eHealth program
- National Natural Science Foundation of China [81771910]
- Beijing Natural Science Foundation in China [4152033]
- Technology and Innovation Commission of Shenzhen in China [shenfagai2016-627]
- Beijing Young Talent Project in China
- Fundamental Research Funds for the Central Universities of China from the State Key Laboratory of Software Development Environment in Beihang University in China [SKLSDE-2017ZX-08]
- 111 Project in China [B13003]
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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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