BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix
This addresses the high cost of immunohistochemical techniques for breast cancer diagnosis, though the method contribution is described as minor.
The authors tackled the problem of expensive HER2 evaluation in breast cancer by creating a benchmark dataset (BCI) of 4870 registered HE-IHC image pairs and developing a pyramid pix2pix method that achieves better translation results than current algorithms.
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.