Deep learning in digital pathology image analysis: a survey.
10.1007/s11684-020-0782-9
- Author:
Shujian DENG
1
;
Xin ZHANG
1
;
Wen YAN
1
;
Eric I-Chao CHANG
2
;
Yubo FAN
1
;
Maode LAI
3
;
Yan XU
4
Author Information
1. School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.
2. Microsoft Research Asia, Beijing, 100080, China.
3. Department of Pathology, School of Medicine, Zhejiang University, Hangzhou, 310007, China.
4. School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China. xuyan04@gmail.com.
- Publication Type:Journal Article
- Keywords:
classification;
deep learning;
detection;
pathology;
segmentation
- From:
Frontiers of Medicine
2020;14(4):470-487
- CountryChina
- Language:English
-
Abstract:
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.
