Dual Contrastive Learning Guided Pathological Image Re-Staining

Abstract

Pathological virtual re-staining is a valuable research topic in AI-aided diagnosis, as it reduces the need for costly and time-consuming physical staining. However, existing methods still suffer from the insufficient ability to preserve tissue microstructure and cellular details, making the generated images less convincing. In this paper, we propose a CycleGAN-based dual contrastive learning re-staining method called DCLRStain. DCLRStain establishes dual contrastive learning between the source and re-stained image domains, conducting negative sampling within each image pair from both domains. It guides the model’s attention to finer content such as cellular details. Meanwhile, DCLRStain introduces a structural similarity-based loss term that further forces the tissue microstructure to be consistent between the source and re-stained images. Experimental results demonstrate that DCLRStain yields competitive quantitative scores compared to state-of-the-art models and maintains superior qualitative performance. Moreover, DCLRStain achieves higher accuracy in the downstream classification task.