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

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