双对齐多任务特征引导的虚拟免疫组化染色
Virtual Immunohistochemistry Staining with Dual-Aligned Multi-Task Feature Guidance
面向H&E到IHC虚拟染色，提出用辅助任务特征做引导，并通过聚类与主被动匹配构建对齐矩阵来对齐H&E与IHC特征，可复用于存在空间错位的跨模态图像翻译与配准任务。
原文依据 we generate an alignment matrix that aligns H&E and IHC features
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论在苏木精-伊红（H&E）到虚拟免疫组化（IHC）染色中，配对图像使监督学习成为可能，但存在固有的空间错位，限制了像素级约束。因此，越来越多的方法利用配对数据引入辅助任务以提供互补监督。然而，现有方法在很大程度上忽略了辅助任务模型中蕴含的丰富语义信息。本文提出一种由双对齐多任务特征引导的虚拟IHC染色新框架，充分挖掘辅助任务中的语义线索。为实现有效引导，我们解决两个障碍：（1）配对的H&E与IHC特征表示之间的空间失配；（2）辅助任务特征与虚拟染色特征之间的任务鸿沟。为解决空间失配，我们生成一个对齐矩阵来对齐H&E与IHC特征。具体而言，我们首先引入结构增强学习，以恢复虚拟IHC图像中受染色不准确影响区域的语义一致性。然后，我们分别对虚拟IHC图像与真实IHC图像的特征进行聚类，并利用主被动匹配机制建立语义对应关系。这确保只有语义对齐的区域才被匹配，从而降低染色变异性对对对齐矩阵的影响。为弥合任务鸿沟，我们引入任务鸿沟对齐模块，其训练原则是：若辅助特征能提升虚拟IHC染色模型的性能，则认为其已对齐。在两个公开数据集、四种生物标志物上的大量实验验证了本框架的有效性。代码见 https://github.com/U-RBook/VSMT。
英文原文摘要 1715 字符
In hematoxylin-eosin (H&E) to virtual immunohistochemistry (IHC) staining, paired images enable supervised learning but suffer from inherent spatial dislocation, limiting pixel-level constraints. Thus, auxiliary tasks have been increasingly employed with paired data to provide complementary supervision. However, existing methods largely overlook the rich semantic information embedded in auxiliary task models. This paper proposes a novel framework for virtual IHC staining guided by dual-aligned multi-task features, which fully explores semantic cues from auxiliary tasks. To realize effective guidance, we address two obstacles: (1) the spatial mismatch between paired H&E and IHC feature representations; (2) the task gap between auxiliary task features and virtual staining features. To resolve the spatial mismatch, we generate an alignment matrix that aligns H&E and IHC features. Specifically, we first introduce structure-enhanced learning to restore semantic consistency in regions affected by inaccurate staining in virtual IHC images. Then, we separately cluster features from virtual IHC and real IHC images, and establish semantic correspondences using an active-passive matching mechanism. This ensures that only semantically aligned regions are matched, reducing the impact of staining variability on the alignment matrix. To bridge the task gap, we introduce a task-gap alignment module trained under the principle that auxiliary features are considered aligned if they improve the performance of the virtual IHC staining model. Extensive experiments on two public datasets with four biomarkers demonstrate the effectiveness of our framework. Code is available at https://github.com/U-RBook/VSMT.