Texture enhanced generative adversarial network for stain normalisation in histopathology images

Digitised histopathology image analysis has drawn researchers’ attention over recent years. However, stain variation due to several factors can be a significant hurdle for the diagnosis process. Stain normalisation can be used as an effective method to address this issue but most existing methods require careful selection of a reference image. In this work, we propose a texture enhanced pix2pix generative adversarial network (TESGAN), which takes higher contrast hematoxylin components as input and includes a novel loss function to guide the generator to produce higher quality images without the need for reference images. We implement our method as a pre-processing approach for an isocitrate dehydrogenase (IDH) mutation status classification task. Evaluated on The Cancer Genome Atlas (TCGA) glioma cohorts, the proposed model achieves Area Under Curve (AUC) of 0.967, which substantially outperforms the current state-of the-art.