Computational analysis of multiplexed immunofluorescence histology data is emerging as an important method for understanding the tumour micro-environment in cancer. This work presents HEMIT, a dataset designed for translating Hematoxylin and Eosin (H&E) sections to multiplex-immunohistochemistry (mIHC) images, simultaneously featuring DAPI, CD3, and panCK markers. Distinctively, HEMIT’s mIHC images are multi-component and cellular-level aligned with H&E, enriching supervised stain translation tasks. To our knowledge, HEMIT is the first publicly available cellular-level aligned dataset that enables H&E to multi-target mIHC image translation. This dataset provides the computer vision community with a valuable resource to develop novel computational methods which have the potential to gain new insights from H&E slide archives. We also propose a new dual-branch generator architecture combining residual Convolutional Neural Networks (CNNs) and Swin Transformers, with a feature map fusion module to integrate information from both branches. This architecture achieves superior translation outcomes compared to other popular algorithms. Evaluations on the HEMIT dataset show it outperforms pix2pixHD, pix2pix, U-Net, and ResNet, with the highest scores in Structural Similarity Index Measure (SSIM), Pearson correlation (R), and Peak Signal-to-Noise Ratio (PSNR). Additionally, we discuss the limitations of commonly used metrics in certain stain translation scenarios and provide recommendations for future use. Additionally, we also designed a downstream analysis to further validate the quality and utility of the generated mIHC images from a clinical-focused perspective. These results set a new benchmark in the field of stain translation tasks. The proposed dataset can be accessed at: https://github.com/BianChang/HEMIT-DATASET .