  H&E-to-IHC stain translation techniques offer a promising solution for
precise cancer diagnosis, especially in low-resource regions where there is a
shortage of health professionals and limited access to expensive equipment.
Considering the pixel-level misalignment of H&E-IHC image pairs, current
research explores the pathological consistency between patches from the same
positions of the image pair. However, most of them overemphasize the
correspondence between domains or patches, overlooking the side information
provided by the non-corresponding objects. In this paper, we propose a
Mix-Domain Contrastive Learning (MDCL) method to leverage the supervision
information in unpaired H&E-to-IHC stain translation. Specifically, the
proposed MDCL method aggregates the inter-domain and intra-domain pathology
information by estimating the correlation between the anchor patch and all the
patches from the matching images, encouraging the network to learn additional
contrastive knowledge from mixed domains. With the mix-domain pathology
information aggregation, MDCL enhances the pathological consistency between the
corresponding patches and the component discrepancy of the patches from the
different positions of the generated IHC image. Extensive experiments on two
H&E-to-IHC stain translation datasets, namely MIST and BCI, demonstrate that
the proposed method achieves state-of-the-art performance across multiple
metrics.
