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.

通过将免疫组化数据与 H&E 染色图像相结合，将其纳入机器学习模型中，可以提高癌症分级的预测结果。提出的框架在公共数据集 BCI 上取得了令人印象深刻的 0.953 的高准确率。

IHC 在 H&E 整片图像分析中的应用：通过两阶段多模态双线性汇聚融合提高癌症分级

IHC Matters: Incorporating IHC analysis to H&E Whole Slide Image  Analysis for Improved Cancer Grading via Two-stage Multimodal Bilinear  Pooling Fusion