A High-Resolution Digital Pathological Image Staining Style Transfer Model Based on Gradient Guidance

Digital pathology images have long been regarded as the gold standard for cancer diagnosis in clinical medicine. A highly generalized digital pathological image diagnosis system can provide strong support for cancer diagnosis, help to improve the diagnostic efficiency and accuracy of doctors, and has important research value. The whole slide image of different centers can lead to very large staining differences due to different scanners and dyes, which pose a challenge to the generalization performance of the model application in multi-center data testing. In order to achieve the normalization of multi-center data, this paper proposes a style transfer algorithm based on an adversarial generative network for high-resolution images. The gradient-guided dye migration model proposed in this paper introduces a gradient-enhanced regularized term in the loss function design of the algorithm. A style transfer algorithm was applied to the source data, and the diagnostic performance of the multi-example learning model based on the domain data was significantly improved by validation in the pathological image datasets of two centers. The proposed method improved the AUC of the best classification model from 0.8856 to 0.9243, and another set of experiments improved the AUC from 0.8012 to 0.8313.