Advances in Staining Processing of Histological Pathology Images in Deep Learning

LUO Shihuan, LIU Zhiming, YANG Biwen, GUO Zhouyi

Acta Laser Biology Sinica ›› 2022, Vol. 31 ›› Issue (6) : 481-487.

PDF(3133 KB)
PDF(3133 KB)
Acta Laser Biology Sinica ›› 2022, Vol. 31 ›› Issue (6) : 481-487.

Advances in Staining Processing of Histological Pathology Images in Deep Learning

Author information +
History +

Abstract

Abstract: Deep learning allows software to assist in diagnosis to be developed and applied more aggressively and efficiently, whereas the color variability of histopathology images degrades the performance of these algorithms. Stain normalization can address image heterogeneity arising from scanner effects, different staining methods, patient’s disease states, staining times, and other factors. Virtual staining can eliminate slide staining and reduce slide preparation steps, reducing sample preparation time for the clinic and saving significant costs. In the absence of annotated training data, pathology image data augmentation is performed by creating artificial samples with realistic texture, color and style to facilitate network training. In this paper, we made a review on staining processing of histological pathology images in deep learning pathology analysis to provide a reference for histological pathology maps in clinical applications and research.
Key words: histological pathology images; staining normalization; virtual staining; data enhancement; deep learning
(Acta Laser Biology Sinica, 2022, 31(6): 481-487)

Cite this article

Download Citations
LUO Shihuan, LIU Zhiming, YANG Biwen, GUO Zhouyi. Advances in Staining Processing of Histological Pathology Images in Deep Learning[J]. Acta Laser Biology Sinica. 2022, 31(6): 481-487
PDF(3133 KB)

Accesses

Citation

Detail

Sections
Recommended

/