Score-based Diffusion Model for Unpaired Virtual Histology Staining

• URL: http://arxiv.org/abs/2506.23184v1
• Date: Sun, 29 Jun 2025 11:02:45 GMT
• Title: Score-based Diffusion Model for Unpaired Virtual Histology Staining
• Authors: Anran Liu, Xiaofei Wang, Jing Cai, Chao Li,
• Abstract summary: Hematoxylin and eosin (H&E) staining visualizes histology but lacks specificity for diagnostic markers.<n>Hematoxylin and eosin (H&E) staining provides protein-targeted staining but is restricted by tissue availability and antibody specificity.<n>Virtual staining, i.e., translating the H&E image to its IHC counterpart while preserving tissue structure, is promising for efficient IHC generation.<n>This study proposes a mutual-information (MI)-guided score-based diffusion model for unpaired virtual staining.
• Score: 7.648204151998162
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hematoxylin and eosin (H&E) staining visualizes histology but lacks specificity for diagnostic markers. Immunohistochemistry (IHC) staining provides protein-targeted staining but is restricted by tissue availability and antibody specificity. Virtual staining, i.e., computationally translating the H&E image to its IHC counterpart while preserving the tissue structure, is promising for efficient IHC generation. Existing virtual staining methods still face key challenges: 1) effective decomposition of staining style and tissue structure, 2) controllable staining process adaptable to diverse tissue and proteins, and 3) rigorous structural consistency modelling to handle the non-pixel-aligned nature of paired H&E and IHC images. This study proposes a mutual-information (MI)-guided score-based diffusion model for unpaired virtual staining. Specifically, we design 1) a global MI-guided energy function that disentangles the tissue structure and staining characteristics across modalities, 2) a novel timestep-customized reverse diffusion process for precise control of the staining intensity and structural reconstruction, and 3) a local MI-driven contrastive learning strategy to ensure the cellular level structural consistency between H&E-IHC images. Extensive experiments demonstrate the our superiority over state-of-the-art approaches, highlighting its biomedical potential. Codes will be open-sourced upon acceptance.

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