StainFlash: Deep-Compression Linear-Attention Flow Matching with Pathological Representation Fusion for H&E-to-IHC Virtual Staining

Immunohistochemical (IHC) staining reveals molecular information essential for accurate pathological diagnosis, yet it requires costly and time-consuming chemical procedures. Virtual staining computationally synthesizes IHC images from hematoxylin-eosin (H&E) images stained with low chemical cost, offering a cost-effective alternative. Despite remarkable progress in virtual staining based on recent diffusion models, achieving high-quality, high-resolution H&E-to-IHC staining with low inference latency and computational cost remains a challenge. This paper presents \ours_name, an efficient framework that leverages a flow-matching network with a deep compression autoencoder and linear attention to overcome the latency and memory constraints of high-resolution virtual staining. To enhance pathological fidelity, we propose a timestep-aware lightweight pathology encoder that adaptively modulates the layout embedding extracted from H&E images, along with a novel **k**nowledge-**i**ntensive **i**mplicit representations **f**usion (KIIF) module that effectively fuses knowledge-intensive implicit representations from multiple pathology foundation models into the flow matching network. Extensive experiments on four datasets stained with various biomarkers demonstrate that \ours_name outperforms existing approaches in high-quality, high-resolution H&E-to-IHC staining, with substantially reduced latency and computational cost. Ablation studies also validate \ours_name's novel designs. Our code and trained models will be released.