Abstract: We leverage paired multiplex immunofluorescence (mpIF) imaging to identify cell types in hematoxylin and eosin (H$\&$E) stained images. By synergizing the strengths of these two imaging modalities, our pipeline enables accurate cell-type annotation in H$\&$E images. This breakthrough allows for the creation of a large, annotated H$\&$E dataset, significantly increasing the scalability of training data generation for machine learning models. This expansion of the dataset is especially crucial for training highly effective deep learning models, as it provides a wealth of more diverse, and representative samples, leading to improved performance and generalization. The pipeline’s ability to generate such a large, annotated dataset offers a valuable resource for detailed analysis and characterization of cell populations, facilitating advanced machine learning applications in pathology and biomedical imaging.