用病理大模型指导，一模型生成多种免疫染色结果。
UNIStainNet: Foundation-Model-Guided Virtual Staining of H&E to IHC
- 以大模型空间特征为条件，指导染色转换的语义准确性
- 单模型同时生成四种免疫染色，性能超越独立训练的多模型
- 在肿瘤外组织上仍有系统性误差，适合临床辅助诊断
从常规H&E图像虚拟生成免疫组化（IHC）染色可加速诊断，提供分子层面的初步信息，减少因组织有限而重复切片的需求。现有方法通过对比学习、原型匹配或域对齐提升真实感，但生成器未直接接受病理大模型的指导。本文提出UNIStainNet，基于冻结的病理大模型UNI的密集空间标记，采用SPADE-UNet结构，为染色转换提供组织级语义引导。设计的错位感知损失保证染色定量准确性，学习到的染色嵌入使单一模型可同时服务多个IHC标记。在MIST数据集上，UNIStainNet在所有四个标记（HER2、Ki67、ER、PR）上均达到最优分布指标，且仅需一个统一模型，而此前方法通常需为每种标记单独训练。在BCI数据集上也表现最佳。按组织类型分层的失败分析显示，剩余错误具有系统性，集中于非肿瘤组织。代码已开源。
原文摘要 · Abstract (English)
Virtual immunohistochemistry (IHC) staining from hematoxylin and eosin (H&E) images can accelerate diagnostics by providing preliminary molecular insight directly from routine sections, reducing the need for repeat sectioning when tissue is limited. Existing methods improve realism through contrastive objectives, prototype matching, or domain alignment, yet the generator itself receives no direct guidance from pathology foundation models. We present UNIStainNet, a SPADE-UNet conditioned on dense spatial tokens from a frozen pathology foundation model (UNI), providing tissue-level semantic guidance for stain translation. A misalignment-aware loss suite preserves stain quantification accuracy, and learned stain embeddings enable a single model to serve multiple IHC markers simultaneously. On MIST, UNIStainNet achieves state-of-the-art distributional metrics on all four stains (HER2, Ki67, ER, PR) from a single unified model, where prior methods typically train separate per-stain models. On BCI, it also achieves the best distributional metrics. A tissue-type stratified failure analysis reveals that remaining errors are systematic, concentrating in non-tumor tissue. Code is available at https://github.com/facevoid/UNIStainNet.
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