Mix-Domain Contrastive Learning for Unpaired H&E-to-IHC Stain Translation

TL;DR

This paper introduces a novel Mix-Domain Contrastive Learning approach for unpaired H&E-to-IHC stain translation, improving pathological consistency and component accuracy in medical image translation tasks.

Contribution

The paper proposes a new MDCL method that leverages unpaired data by aggregating inter- and intra-domain pathology information for better stain translation.

Findings

Achieves state-of-the-art performance on MIST and BCI datasets.

Enhances pathological consistency between patches.

Improves component accuracy in generated images.

Abstract

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.