Abstract:Histological staining is time-consuming and irreversible in clinical practice, which leads to a relatively limited quantity of renal pathology images, thus restricts the application of medical diagnosis and deep learning method. A deep learning model based on generative adversarial networks was proposed, style transfer between different staining techniques was achieved through a single training process. Subsequently, a multi-domain staining style transfer model was introduced into the glomerulus detection workflow. The staining transformations were performed using the staining transfer model in experiments, and the accuracy and generalization of the glomerulus detection model were improved by the the mutual complementarity of staining features from different styles. The experimental results show that the the images generated by the multi-domain staining style transfer model are reliable and effective in improving glomerulus detection performance.