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.

翻译：染色在组织病理学中对可视化特定组织特征至关重要，其中苏木精-伊红（H&E）染色是临床金标准。然而，病理学家经常需要利用多种特殊染色技术来诊断特定形态结构。为这些玻片维护准确的元数据，对于临床档案的质量控制以及计算病理学数据集的完整性具有关键意义。本研究比较了两种基于全切片图像的自动染色分类方法，涵盖了我们机构最常用的14种特殊染色以及标准H&E染色和冰冻切片H&E染色。我们评估了多示例学习（MIL）流程和一种新提出的基于缩略图的轻量化方法。在内部测试数据上，MIL方法取得了最佳性能（16类宏F1：0.941；14类合并宏F1：0.969），而缩略图方法仍保持竞争力（分别为0.897和0.953）。在外部TCGA数据上，缩略图模型展现出最优的泛化能力（加权F1：0.843 vs. MIL的0.807）。缩略图方法还将处理吞吐量提升了两个数量级（5.635张/秒 vs. MIL全分块处理的0.018张/秒）。我们得出结论：基于缩略图的分类方法为数字病理工作流程中的常规视觉质量控制提供了可扩展且稳健的解决方案。