跨中心病理染色差异大，该研究系统比较了8种归一化方法。
Staining normalization in histopathology: Method benchmarking using multicenter dataset
- 用66个实验室的组织样本构建多中心染色数据集，控制其他变量。
- 深度学习方法（如Pix2Pix）在定量和定性评估中表现更优。
- 结果可帮助提升AI模型在真实世界中的泛化能力，适合医学影像研究者。
苏木精-伊红（H&E）染色是组织分析的金标准，但不同实验室制备的切片外观差异显著，给病理医生和基于AI的下游分析带来挑战。为深入研究此问题，我们收集了一个独特的多中心组织图像数据集：结肠、肾和皮肤组织块被分发至66个不同实验室进行常规H&E染色，其他影响组织外观的因素保持恒定以隔离染色变异。利用该数据集，我们对比了八种不同的染色归一化方法，包括四种传统方法（直方图匹配、Macenko、Vahadane、Reinhard）和两种基于深度学习的方法（CycleGAN和Pix2Pix，各含两个变体）。通过定量与定性评估综合比较其性能。该数据集揭示的跨实验室染色差异可指导通过多样化训练数据提升模型泛化性的策略。
原文摘要 · Abstract (English)
Hematoxylin and Eosin (H&E) has been the gold standard in tissue analysis for decades, however, tissue specimens stained in different laboratories vary, often significantly, in appearance. This variation poses a challenge for both pathologists' and AI-based downstream analysis. Minimizing stain variation computationally is an active area of research. To further investigate this problem, we collected a unique multi-center tissue image dataset, wherein tissue samples from colon, kidney, and skin tissue blocks were distributed to 66 different labs for routine H&E staining. To isolate staining variation, other factors affecting the tissue appearance were kept constant. Further, we used this tissue image dataset to compare the performance of eight different stain normalization methods, including four traditional methods, namely, histogram matching, Macenko, Vahadane, and Reinhard normalization, and two deep learning-based methods namely CycleGAN and Pixp2pix, both with two variants each. We used both quantitative and qualitative evaluation to assess the performance of these methods. The dataset's inter-laboratory staining variation could also guide strategies to improve model generalizability through varied training data
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