Title
Stain Standardization Capsule for Application-Driven Histopathological Image Normalization
Abstract
Color consistency is crucial to developing robust deep learning methods for histopathological image analysis. With the increasing application of digital histopathological slides, the deep learning methods are probably developed based on the data from multiple medical centers. This requirement makes it a challenging task to normalize the color variance of histopathological images from different medical centers. In this paper, we propose a novel color standardization module named stain standardization capsule based on the capsule network and the corresponding dynamic routing algorithm. The proposed module can learn and generate uniform stain separation outputs for histopathological images in various color appearance without the reference to manually selected template images. The proposed module is light and can be jointly trained with the application-driven CNN model. The proposed method was validated on three histopathology datasets and a cytology dataset, and was compared with state-of-the-art methods. The experimental results have demonstrated that the SSC module is effective in improving the performance of histopathological image analysis and has achieved the best performance in the compared methods.
Year
DOI
Venue
2021
10.1109/JBHI.2020.2983206
IEEE Journal of Biomedical and Health Informatics
Keywords
DocType
Volume
Algorithms,Coloring Agents,Humans,Image Processing, Computer-Assisted,Reference Standards,Staining and Labeling
Journal
25
Issue
ISSN
Citations 
2
2168-2194
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Yushan Zheng1346.11
Zhiguo Jiang232145.58
Haopeng Zhang34714.75
Fengying Xie4153.31
Dingyi Hu511.04
Shujiao Sun611.04
Jun Shi762.15
Chenghai Xue800.34