HistDiST: Histopathological Diffusion-based Stain Transfer

• URL: http://arxiv.org/abs/2505.06793v2
• Date: Fri, 19 Sep 2025 09:55:38 GMT
• Title: HistDiST: Histopathological Diffusion-based Stain Transfer
• Abstract summary: HistDiST is a Latent Diffusion Model (LDM) based framework for high-fidelity H&E-to-IHC translation.<n>HistDiST significantly outperforms existing methods, achieving a 28% improvement in MRA on evaluations H&E-to-Ki6767.
• Score: 4.936165689668498
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hematoxylin and Eosin (H&E) staining is the cornerstone of histopathology but lacks molecular specificity. While Immunohistochemistry (IHC) provides molecular insights, it is costly and complex, motivating H&E-to-IHC translation as a cost-effective alternative. Existing translation methods are mainly GAN-based, often struggling with training instability and limited structural fidelity, while diffusion-based approaches remain underexplored. We propose HistDiST, a Latent Diffusion Model (LDM) based framework for high-fidelity H&E-to-IHC translation. HistDiST introduces a dual-conditioning strategy, utilizing Phikon-extracted morphological embeddings alongside VAE-encoded H&E representations to ensure pathology-relevant context and structural consistency. To overcome brightness biases, we incorporate a rescaled noise schedule, v-prediction, and trailing timesteps, enforcing a zero-SNR condition at the final timestep. During inference, DDIM inversion preserves the morphological structure, while an eta-cosine noise schedule introduces controlled stochasticity, balancing structural consistency and molecular fidelity. Moreover, we propose Molecular Retrieval Accuracy (MRA), a novel pathology-aware metric leveraging GigaPath embeddings to assess molecular relevance. Extensive evaluations on MIST and BCI datasets demonstrate that HistDiST significantly outperforms existing methods, achieving a 28% improvement in MRA on the H&E-to-Ki67 translation task, highlighting its effectiveness in capturing true IHC semantics.

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