For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan. From the perspective of saving manpower, material and time costs, directly generating IHC-stained images from hematoxylin and eosin (H&E) stained images is a valuable research direction. Therefore, we held the breast cancer immunohistochemical image generation challenge, aiming to explore novel ideas of deep learning technology in pathological image generation and promote research in this field. The challenge provided registered H&E and IHC-stained image pairs, and participants were required to use these images to train a model that can directly generate IHC-stained images from corresponding H&E-stained images. We selected and reviewed the five highest-ranking methods based on their PSNR and SSIM metrics, while also providing overviews of the corresponding pipelines and implementations. In this paper, we further analyze the current limitations in the field of breast cancer immunohistochemical image generation and forecast the future development of this field. We hope that the released dataset and the challenge will inspire more scholars to jointly study higher-quality IHC-stained image generation.

翻译：对于浸润性乳腺癌，常采用免疫组化（IHC）技术检测乳腺组织中人类表皮生长因子受体2（HER2）的表达水平，以制定精准治疗方案。从节省人力、物力和时间成本的角度出发，直接由苏木精-伊红（H&E）染色图像生成IHC染色图像是一个有价值的研究方向。为此，我们举办了乳腺癌免疫组化图像生成挑战赛，旨在探索深度学习技术在病理图像生成中的新思路，并推动该领域的研究进展。挑战赛提供了配准后的H&E与IHC染色图像对，要求参赛者利用这些图像训练模型，使其能够直接从对应H&E染色图像生成IHC染色图像。我们根据PSNR和SSIM指标评选出排名前五的方法并进行评述，同时概述了相应流程与实现方式。本文进一步分析了当前乳腺癌免疫组化图像生成领域存在的局限性，并预测了该领域的未来发展方向。我们期望所发布的数据集及挑战赛能够激励更多学者共同研究更高质量的IHC染色图像生成方法。