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.

翻译：H&E至IHC染色转换技术为精准癌症诊断提供了一种前景广阔的解决方案，尤其在医疗专业人员短缺且昂贵设备获取受限的低资源地区。考虑到H&E-IHC图像对在像素级上的未对齐问题，当前研究探索了来自图像对相同位置图像块之间的病理学一致性。然而，大多数方法过度强调域间或图像块间的对应关系，忽视了非对应对象提供的辅助信息。本文提出了一种混合域对比学习（MDCL）方法，以利用非配对H&E至IHC染色转换中的监督信息。具体而言，所提出的MDCL方法通过估计锚定图像块与匹配图像中所有图像块之间的相关性，聚合了域间和域内的病理学信息，从而促使网络从混合域中学习额外的对比知识。借助混合域病理信息聚合，MDCL增强了对应图像块之间的病理学一致性，以及生成的IHC图像中不同位置图像块的成分差异性。在两个H&E至IHC染色转换数据集（即MIST和BCI）上进行的大量实验表明，所提方法在多项指标上均达到了最先进的性能。