Deep learning in digital pathology image analysis: a survey

doi:10.1007/s11684-020-0782-9

Frontiers of Medicine

2020, Vol. 14

Issue (4): 470-487 https://doi.org/10.1007/s11684-020-0782-9

本期目录

Deep learning in digital pathology image analysis: a survey

Shujian Deng^1,^2,³, Xin Zhang^1,^2,³, Wen Yan^1,^2,³, Eric I-Chao Chang⁴, Yubo Fan^1,^2,³, Maode Lai⁵, Yan Xu^1,^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.

Key words： pathology deep learning segmentation detection classification

收稿日期: 2019-08-19 出版日期: 2020-08-26

Corresponding Author(s): Yan Xu

引用本文:

. [J]. Frontiers of Medicine, 2020, 14(4): 470-487.
Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu. Deep learning in digital pathology image analysis: a survey. Front. Med., 2020, 14(4): 470-487.

链接本文:

https://academic.hep.com.cn/fmd/CN/10.1007/s11684-020-0782-9
https://academic.hep.com.cn/fmd/CN/Y2020/V14/I4/470

Fig.1

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

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