用小图快速自动识别病理切片染色类型，提升数字病理流程效率。
Efficient Special Stain Classification
- 基于高分辨率切片的小图快照进行染色分类，避免全图分析的计算开销。
- 在16类染色上达到0.941的宏平均F1，外部数据集上表现优于传统方法。
- 速度比传统方法快100倍，适合大规模病理图像的质量控制场景。
染色是组织病理学中可视化特定组织特征的关键手段，其中苏木精-伊红（H&E）是临床标准。然而，病理科医生常需使用多种特殊染色来诊断特定形态。准确维护这些切片的元数据对临床档案质量控制和计算病理数据集的完整性至关重要。本文比较了两种基于全切片图像的自动化染色分类方法：多实例学习（MIL）管道与提出的轻量级小图快照方法，覆盖本院最常用的14种特殊染色及标准和冰冻切片H&E。内部测试中，MIL表现最佳（16类宏平均F1: 0.941；14类合并后为0.969），小图方法亦具竞争力（分别为0.897和0.953）。在外部TCGA数据上，小图模型泛化能力更优（加权F1: 0.843 vs. 0.807）。此外，小图方法吞吐量提升两个数量级（5.635对比0.018滑片/秒）。结论：基于小图的分类提供了一种可扩展且鲁棒的数字病理常规质检方案。
原文摘要 · Abstract (English)
Stains are essential in histopathology to visualize specific tissue characteristics, with Haematoxylin and Eosin (H&E) serving as the clinical standard. However, pathologists frequently utilize a variety of special stains for the diagnosis of specific morphologies. Maintaining accurate metadata for these slides is critical for quality control in clinical archives and for the integrity of computational pathology datasets. In this work, we compare two approaches for automated classification of stains using whole slide images, covering the 14 most commonly used special stains in our institute alongside standard and frozen-section H&E. We evaluate a Multi-Instance Learning (MIL) pipeline and a proposed lightweight thumbnail-based approach. On internal test data, MIL achieved the highest performance (macro F1: 0.941 for 16 classes; 0.969 for 14 merged classes), while the thumbnail approach remained competitive (0.897 and 0.953, respectively). On external TCGA data, the thumbnail model generalized best (weighted F1: 0.843 vs. 0.807 for MIL). The thumbnail approach also increased throughput by two orders of magnitude (5.635 vs. 0.018 slides/s for MIL with all patches). We conclude that thumbnail-based classification provides a scalable and robust solution for routine visual quality control in digital pathology workflows.
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