MIHCGENER: A Framework for Multiple Immunohistochemical Image Generation Based on the Combination of Pathological Foundation Model and Generative Model

The tumor microenvironment (TME) is important to the treatment and prognosis of cancer. Multiplex Immunohistochemical (mIHC) images can display the expression of multiple biomolecular markers while maintaining spatial location, making it an effective tool for TME analysis. However, acquiring mlHC images demands stringent laboratory conditions, extensive time and resources. In this study, we proposed an end-to-end generative framework to transform hematoxylin and eosin (H&E) images into mIHC images. In addition, we introduced a pathological foundation model into the feature encoding of pathological image generation to enhance the performance of our framework and proved the superiority and necessity of the foundation model. Our method successfully generated mIHC images, achieved state-of-the-art (SOTA) performance against popular methods and maintained robustness on cross-cancer. The framework we proposed is expected to provide a more efficient and cheaper tool for the observation and evaluation of TME in pathology.