  Immunohistochemistry (IHC) plays a crucial role in pathology as it detects
the over-expression of protein in tissue samples. However, there are still
fewer machine learning model studies on IHC's impact on accurate cancer
grading. We discovered that IHC and H\&E possess distinct advantages and
disadvantages while possessing certain complementary qualities. Building on
this observation, we developed a two-stage multi-modal bilinear model with a
feature pooling module. This model aims to maximize the potential of both IHC
and HE's feature representation, resulting in improved performance compared to
their individual use. Our experiments demonstrate that incorporating IHC data
into machine learning models, alongside H\&E stained images, leads to superior
predictive results for cancer grading. The proposed framework achieves an
impressive ACC higher of 0.953 on the public dataset BCI.
