Contrastive Learning Based Stain Normalization Across Multiple Tumor in Histopathology

Generative adversarial network (GAN) has been a prevalence in color normalization techniques to assist deep learning analysis in H&E stained histopathology images. The widespread adoption of GAN has effectively released pathologists from the heavy manual workload in the conventional template image selection. However, the transformation might cause significant information loss, or generate undesirable results such as mode collapse in all likelihood, which may affect the performance in the succeeding diagnostic task. To address the issue, we propose a contrastive learning method with a color-variation constraint, which maximally retains the recognizable phenotypic features at the training of a color-normalization GAN. In a self-supervised manner, the discriminative tissue patches across multiple types of tumors are clustered, taken as the salient input to feed the GAN. Empirically, the model is evaluated by public datasets of large cohorts on different cancer diseases from TCGA and Camelyon16. We show better phenotypical recognizability along with an improved performance in the histology image classification.