  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.
