用于三级淋巴结构检测的基于掩码引导对抗迁移学习的组织学虚拟染色
Histology Virtual Staining with Mask-Guided Adversarial Transfer Learning for Tertiary Lymphoid Structure Detection
AI总结：
针对三级淋巴结构检测依赖昂贵免疫组化染色的问题，提出VIPA-Net框架，通过掩码引导对抗迁移学习将H&E染色虚拟转化为IHC染色，在1019张TCGA数据集上显著提升了TLS检测准确率。
摘要：
组织学三级淋巴结构（TLSs）因与多种实体瘤免疫治疗的疗效相关而日益受到重视。传统上，TLSs的识别和表征依赖免疫组化（IHC）染色技术，使用如针对B细胞的CD20等标记物。尽管IHC具有特异性，苏木精-伊红（H&E）染色提供了一种更易获取且更具成本效益的选择。利用H&E染色切片的普及性，我们引入了一种用于虚拟病理染色的新型Mask-Guided Adversarial Transfer Learning方法。该方法能在无显式标签信息的情况下，巧妙捕捉不同染色条件下各类组织（如细胞核、红细胞、阳性反应区域）间细微的颜色变化，并巧妙合成逼真的类IHC虚拟染色块，甚至能复现阳性反应。此外，我们提出了虚拟IHC病理分析网络（VIPA-Net），这是一个集成了掩码引导迁移模块和基于H&E的虚拟染色TLS检测模块的综合框架。VIPA-Net协同利用H&E染色切片与合成的虚拟IHC块，以增强H&E全切片图像（WSIs）中TLSs的检测。我们使用包含来自癌症基因组图谱（TCGA）的1019张标注切片的综合数据集对该网络进行了评估。实验结果有力地表明，VIPA-Net显著提升了TLS检测的准确率，有效避免了在公共数据集上进行实际CD20染色的需求。
英文摘要：
Histological Tertiary Lymphoid Structures (TLSs) are increasingly recognized for their correlation with the efficacy of immunotherapy in various solid tumors. Traditionally, the identification and characterization of TLSs rely on immunohistochemistry (IHC) staining techniques, utilizing markers such as CD20 for B cells. Despite the specificity of IHC, Hematoxylin-Eosin (H&E) staining offers a more accessible and cost-effective choice. Capitalizing on the prevalence of H&E staining slides, we introduce a novel Mask-Guided Adversarial Transfer Learning method designed for virtual pathological staining. This method adeptly captures the nuanced color variations across diverse tissue types under various staining conditions, such as nucleus, red blood cells, positive reaction regions, without explicit label information, and adeptly synthesizes realistic IHC-like virtual staining patches, even replicating the positive reaction. Further, we propose the Virtual IHC Pathology Analysis Network (VIPA-Net), an integrated framework encompassing a Mask-Guided Transfer Module and an H&E-Based Virtual Staining TLS Detection Module. VIPA-Net synergistically harnesses both H\&E staining slides and the synthesized virtual IHC patches to enhance the detection of TLSs within H&E Whole Slide Images (WSIs). We evaluate the network with a comprehensive dataset comprising 1019 annotated slides from The Cancer Genome Atlas (TCGA). Experimental results compellingly illustrate that the VIPA-Net substantially elevates TLS detection accuracy, effectively circumventing the need for actual CD20 staining across the public dataset.
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