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Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

Postal Subscription Code 80-967

2018 Impact Factor: 1.847

Front. Med.    2020, Vol. 14 Issue (4) : 470-487    https://doi.org/10.1007/s11684-020-0782-9
REVIEW
Deep learning in digital pathology image analysis: a survey
Shujian Deng1,2,3, Xin Zhang1,2,3, Wen Yan1,2,3, Eric I-Chao Chang4, Yubo Fan1,2,3, Maode Lai5, Yan Xu1,2,3,4()
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
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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.

Keywords pathology      deep learning      segmentation      detection      classification     
Corresponding Author(s): Yan Xu   
Just Accepted Date: 28 June 2020   Online First Date: 27 July 2020    Issue Date: 26 August 2020
 Cite this article:   
Shujian Deng,Xin Zhang,Wen Yan, et al. Deep learning in digital pathology image analysis: a survey[J]. Front. Med., 2020, 14(4): 470-487.
 URL:  
https://academic.hep.com.cn/fmd/EN/10.1007/s11684-020-0782-9
https://academic.hep.com.cn/fmd/EN/Y2020/V14/I4/470
Fig.1  Multilevel WSI objects (delineated in red lines).
Reference Year Object Organ Staining/modality Architecture
Semantic segmentation of cellular objects
[37] 2012 Neuronal membranes Brain EM CNN Ensemble of several CNNs with different architectures
[38] 2015 Neuronal cell membranes Brain cervix EM LM U-Net U-net with deformation augmentation
[39] 2015 Nuclei Brain H&E CNN Sparsity constrained convolutional regression
[40] 2015 Cytoplasm and nuclei Cervix H&E CNN Multi-scale CNN and graph partitioning-based method
[41] 2016 Nuclei Brain/pancreas/breast H&E CNN CNN and selection-based sparse shape model
[42] 2016 Nuclei Breast H&E CNN Sparse reconstruction and CNN and morphological operations
[43] 2017 Nuclei Cervix H&E FCN FCN and graph-based approach
[44] 2018 Nuclei FL U-Net U-net with recurrent residual convolutional operations
Cell/nuclei detection
[45] 2015 Cell Breast cervix H&E PC CNN CNN-based structured regression model
[46] 2016 Nuclei Colorectal adenocarcinomas H&E CNN Spatially constrained CNN for regression
[47] 2016 Nuclei Breast H&E SAE Stack SAE
[48] 2018 Cell Breast cervix Neuroendocrine tumor H&E PC Ki-67 FCN Structured regression model based on residual FCN
Mitosis detection
[49] 2013 Mitosis Breast H&E CNN CNN-based pixel classifier
[50] 2014 Breast H&E CNN Combination of CNN and hand-crafted features
[36] 2016 Breast H&E FCN Deep regression network
[51] 2016 Breast H&E FCN Deep cascaded networks
[52] 2016 Breast H&E CNN Incorporated “crowdsourcing” layer into the CNN framework
[53] 2018 Breast H&E FCN Proposal-based deep detection network
[54] 2018 Breast H&E CNN Multi-scale and similarity learning CNN
[55] 2019 Breast H&E FCN Weakly supervised FCN using concentric loss
Cell/nuclei instance segmentation
[56] 2016 Cell Mouse brain TPM U-net U-net with an iterative k-terminal cut algorithm
[57,58] 2016 Cell Cervix FL FCN Proposal-based CNN model
[59] 2016 Nuclei Brain H&E FCN Deep contour-aware network
[60] 2016 Cell Cervix H&E CNN Multi-scale CNN and multiple cell labeling and dynamic multitemplate deformation model
[61] 2017 Nuclei Multiple organs H&E CNN Three-class CNN with nuclear boundaries emphasized
[62] 2018 Nuclei Rat kidney FL CNN 3D distance transform and CNN
[63] 2018 Nuclei Multiple organs H&E U-Net U-net with distance map regression
[64] 2019 Nuclei Multiple organs H&E FCN Contour-aware informative aggregation
Tab.1  Overview of papers using deep learning for cell-level DP image analysis
Reference Year Architecture Methods Postprocessing
[107] 2015 CNN Object-Net to predict foreground and Separator-Net to segment individuals Yes
[108] 2016 CNN Pixel-level classification Yes
[59] 2016 FCN FCN-based semantic segmentation and contour prediction Yes
[106,109] 2016 FCN FCN-based semantic segmentation and HED side convolution channel for contour detection No
[110] 2016 FCN Combine two losses into a unified topology-aware loss No
[111] 2016 CNN Multitask learning for gland grading and segmentation No
Tab.2  Overview of papers using deep learning for gland-level DP image analysis
Fig.2  H&E stained pathology images of colon glands [100]. (A–D) Benign cases. (E and F) Malignant cases. All the slides are under the same magnification and lighting conditions.
Method Jaccard index Dice index
HC-SVM 0.71 ± 0.11 0.83 ± 0.09
Alexnet+ Googlenet 0.74 ± 0.14 0.84 ± 0.10
HC-SVM+ Alexnet+ Googlenet 0.77 ± 0.11 0.87 ± 0.08
Tab.3  Performance comparison of the three methods based on pure deep learning, hand-crafted features, and their fusion [108], respectively
Reference Year Architecture Region/WSI Organ Task Methods
[131] 2014 CNN Region Colon Classification and segmentation CNN with MIL
[132] 2015 CNN Region Brain/colon Classification and segmentation CNN with feature pooling and selection
[133] 2016 CNN WSI Lung Classification EM-based model with CNN
[134] 2016 CNN Region Prostate Classification Pretrained OverFeat with multilayer feature learning
[135] 2016 CNN Region Breast/colon Classification and segmentation Superpixel-based scheme for generating patches
[136] 2017 CNN WSI Colon Segmentation Combine persistent homology features with CNN features for ensemble learning
[137] 2017 FCN Region Colon Segmentation FCN with constrained deep weak supervision
[138] 2018 CNN WSI Lung/lymph nodes Classification and segmentation ResNet-50 with MIL
[139] 2018 FCN WSI Lung Classification FCN with context-aware feature selection
[140] 2019 CNN Region Breast Classification Pretrained VGG-16 and average pooling of patch-level features for region-level ones
Tab.4  Overview of papers using deep learning for region-level DP image analysis
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