Cross-Domain Image Synthesis: Generating H&E from Multiplexed Biomarker Imaging

While multiplex immunofluorescence (mIF) imaging provides deep, spatially-resolved molecular data, integrating this information with the morphological standard of Hematoxylin & Eosin (H&E) can be very important for obtaining complementary information about the underlying tissue. Generating a virtual H&E stain from mIF data offers a powerful solution, providing immediate morphological context. Crucially, this approach enables the application of the vast ecosystem of H&E-based computer-aided diagnosis (CAD) tools to analyze rich molecular data, bridging the gap between molecular and morphological analysis. In this work, we investigate the use of a multi-level Vector-Quantized Generative Adversarial Network (VQGAN) to create high-fidelity virtual H&E stains from mIF images. We rigorously evaluated our VQGAN against a standard conditional GAN (cGAN) baseline on two publicly available colorectal cancer datasets, assessing performance on both image similarity and functional utility for downstream analysis. Our results demonstrate that VQGAN-based models achieve improved performance in both standard image quality metrics and downstream analysis tasks, with VQGAN-generated virtual stains showing enhanced nuclei segmentation accuracy and tissue classification consistency compared to cGAN baselines. This work demonstrates the potential of multi-level vector quantization for integrating high-dimensional molecular data with established morphological analysis workflows.