论文标题
BCI：乳腺癌免疫组织化学图像通过金字塔PIX2PIX生成
BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix
论文作者
论文摘要
人类表皮生长因子受体2（HER2）表达的评估对于制定乳腺癌的精确治疗至关重要。 HER2的常规评估是通过免疫组织化学技术（IHC）进行的，这非常昂贵。因此，我们第一次提出了试图将IHC数据直接与配对的苏木精和曙红（HE）染色图像合成的乳腺癌免疫组织化学（BCI）基准。该数据集包含4870个注册的图像对，涵盖了各种HER2表达水平。基于BCI，作为较小的贡献，我们进一步构建了一种金字塔Pix2Pix图像生成方法，它比其他当前流行算法更好地获得了IHC翻译结果。广泛的实验表明，BCI对现有图像翻译研究提出了新的挑战。此外，BCI还基于合成的IHC图像在HER2表达评估中为未来的病理研究打开了大门。可以从https://bupt-ai-cz.github.io/bci下载BCI数据集。
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.
