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)
引用本文:
罗诗欢,刘智明,杨必文,郭周义. 组织学病理图像在深度学习中染色处理的研究进展[J]. 激光生物学报, 2022, 31(6): 481-487.
LUO Shihuan, LIU Zhiming, YANG Biwen, GUO Zhouyi. Advances in Staining Processing of Histological Pathology Images in Deep Learning. journal1, 2022, 31(6): 481-487.