Immunohistochemistry (IHC) is essential for assessing specific immune biomarkers like Human Epidermal growth-factor Receptor 2 (HER2) in breast cancer. However, the traditional protocols of obtaining IHC stains are resource-intensive, time-consuming, and prone to structural damages. Virtual staining has emerged as a scalable alternative, but it faces significant challenges in preserving fine-grained cellular structures while accurately translating biochemical expressions. Current state-of-the-art methods still rely on Generative Adversarial Networks (GANs) or standard convolutional U-Net diffusion models that often struggle with "structure and staining trade-offs". The generated samples are either structurally relevant but blurry, or texturally realistic but have artifacts that compromise their diagnostic use. In this paper, we introduce HistDiT, a novel latent conditional Diffusion Transformer (DiT) architecture that establishes a new benchmark for visual fidelity in virtual histological staining. The novelty introduced in this work is, a) the Dual-Stream Conditioning strategy that explicitly maintains a balance between spatial constraints via VAE-encoded latents and semantic phenotype guidance via UNI embeddings; b) the multi-objective loss function that contributes to sharper images with clear morphological structure; and c) the use of the Structural Correlation Metric (SCM) to focus on the core morphological structure for precise assessment of sample quality. Consequently, our model outperforms existing baselines, as demonstrated through rigorous quantitative and qualitative evaluations.

翻译：免疫组织化学（IHC）对于评估乳腺癌中人类表皮生长因子受体2（HER2）等特异性免疫生物标志物至关重要。然而，获取IHC染色切片的传统方案资源密集、耗时且易造成结构损伤。虚拟染色技术作为一种可扩展的替代方案应运而生，但在保留精细细胞结构的同时实现生化表达精确翻译面临重大挑战。当前最先进方法仍依赖生成对抗网络（GAN）或标准卷积U-Net扩散模型，这些方法常陷入“结构与染色权衡困境”：生成的样本要么结构相关但模糊，要么纹理真实但存在影响诊断价值的伪影。本文提出HistDiT——一种新颖的潜在条件扩散Transformer（DiT）架构，为虚拟组织学染色建立了视觉保真度的新基准。本工作的创新点包括：a）双流条件化策略，通过VAE编码的潜在空间显式维持空间约束，并借助UNI嵌入提供语义表型引导；b）多目标损失函数，有助于生成具有清晰形态结构的锐利图像；c）采用结构相关性度量（SCM）聚焦核心形态结构，实现样本质量的精确评估。经严谨的定量与定性评估证明，我们的模型全面超越了现有基线方法。