Stain Standardization Capsule: A pre-processing module for histopathological image analysis

Yushan Zheng, Zhiguo Jiang, Haopeng Zhang, Jun Shi, Fengying Xie

Abstract: Color consistency is crucial to developing robust deep learning methods for histopathological image analysis. With the increasing application of digital histopathological images, the deep learning methods are likely developed based on the data from multiple medical centers. This requirement makes it a challenge task to normalize the color variance of histopathological images from different medical centers. In this paper, we proposed a novel color standardization module named stain standardization capsule (SSC) based on the paradigm of 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 SSC module is light and can be trained end-to-end with the application-driven CNN model. The proposed method was validated on two public datasets and compared with the state-of-the-art methods. The experimental results have demonstrated that the SSC module is effective in color normalization for histopathological images and achieves the best performance in the compared methods.

Keywords: Stain standardization, Histopathological image analysis, Digital pathology, Capsules network

TL;DR: A novel stain standardization module for histopahtological image normalization is proposed.