The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. Therefore, for the first time, we propose a breast cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data directly with the paired hematoxylin and eosin (HE) stained images. The dataset contains 4870 registered image pairs, covering a variety of HER2 expression levels. Based on BCI, as a minor contribution, we further build a pyramid pix2pix image generation method, which achieves better HE to IHC translation results than the other current popular algorithms. Extensive experiments demonstrate that BCI poses new challenges to the existing image translation research. Besides, BCI also opens the door for future pathology studies in HER2 expression evaluation based on the synthesized IHC images. BCI dataset can be downloaded from https://bupt-ai-cz.github.io/BCI.
翻译：人类上皮生长因子受体2 (HER2) 表达方式的评估对于制定乳腺癌的精确治疗至关重要。对HER2的常规评估是使用非常昂贵的免疫史化学技术进行的。因此,我们首次提议了乳腺癌免疫生物学化学基准,试图直接与配对的血氧素和Eosin(He) 沾染图像合成IHHC数据。数据集包含4870张已登记的图像配对,涵盖各种HER2表达水平。根据BCI,作为小贡献,我们进一步建立了金字塔像生成方法,使HE对IHC的翻译结果比其他目前流行的算法要好。广泛的实验表明,BCI对现有的图像翻译研究提出了新的挑战。此外,BCI还打开了根据合成IHEC图像进行HER2表达评价的未来病理学研究的大门。BCI数据集可从https://bupt-i-cz.github.io/BCI下载。
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