高效特殊染色分类
Efficient Special Stain Classification
AI总结：
本文提出了一种基于缩略图的高效特殊染色分类方法，在准确性和处理速度上均优于多实例学习方法。
摘要：
染色在组织病理学中至关重要，用于可视化特定组织特征，血清素和伊红（H&E）是临床标准。然而，病理学家经常使用多种特殊染色来诊断特定形态。保持这些切片的准确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至关重要。在本工作中，我们比较了两种用于自动分类染色的全滑片图像方法，涵盖了我们机构最常用的14种特殊染色，以及标准和冷冻切片H&E。我们评估了多实例学习（MIL）管道和一个提出的轻量级缩略图方法。在内部测试数据上，MIL实现了最高性能（宏F1：16类0.941；14类合并0.969），而缩略图方法仍具竞争力（0.897和0.953，分别）。在外部TCGA数据上，缩略图模型泛化最好（加权F1：0.843 vs. 0.807 for MIL）。缩略图方法还提高了处理速度两倍（5.635 vs. 0.018 slides/s for MIL with all patches）。我们得出结论，基于缩略图的分类为数字病理学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。
英文摘要：
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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