HistDiST：基于组织病理学扩散模型的染色转换
HistDiST: Histopathological Diffusion-based Stain Transfer
AI总结：
HistDiST提出基于潜在扩散模型和双重条件策略的H&E到IHC染色转换框架，并引入病理感知指标MRA，在MIST和BCI数据集上显著超越现有方法，Ki67转换任务MRA提升28%。
摘要：
苏木精-伊红（H&E）染色是组织病理学的基石，但缺乏分子特异性。免疫组织化学（IHC）虽然能提供分子层面的信息，但成本高且流程复杂，因此将H&E转换为IHC成为一种具有成本效益的替代方案。现有的转换方法主要基于GAN，常常面临训练不稳定和结构保真度有限的问题，而基于扩散模型的方法尚未得到充分探索。我们提出HistDiST，一个基于潜在扩散模型（LDM）的框架，用于高保真的H&E到IHC转换。HistDiST引入了一种双重条件策略，利用Phikon提取的形态学嵌入以及VAE编码的H&E表示，以确保病理学相关的上下文和结构一致性。为了克服亮度偏差，我们引入了重新缩放的噪声调度、v预测和尾部时间步，在最终时间步强制执行零信噪比（zero-SNR）条件。在推理过程中，DDIM反演保持形态结构，而eta余弦噪声调度引入受控的随机性，在结构一致性和分子保真度之间取得平衡。此外，我们提出了分子检索准确率（MRA），这是一种新的病理感知指标，利用GigaPath嵌入来评估分子相关性。在MIST和BCI数据集上的广泛评估表明，HistDiST显著优于现有方法，在H&E到Ki67转换任务上MRA提高了28%，突显了其在捕捉真实IHC语义方面的有效性。
英文摘要：
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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