面向乳腺癌切片病理淋巴结转移的综合基准
Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis in Breast Cancer Sections
AI总结：
本研究重新处理了Camelyon系列1,399张WSIs，纠正标签并补充专家像素标注，将乳腺癌淋巴结转移筛查升级为四分类任务，为病理学特征提取器与MIL方法提供了新基准。
摘要：
光学显微镜扫描的进步通过将传统组织病理学切片转换为全切片图像（WSIs），为计算病理学（CPath）做出了重大贡献。这一发展使病理学家能够进行全面的数字审查，并加速了针对WSI分析的AI驱动诊断支持。病理学基础模型的最新进展增加了对基准测试任务的需求。Camelyon系列是计算病理学中使用最广泛的开源数据集之一。然而，其标签的质量、可及性和临床相关性尚未得到全面评估。在本研究中，我们重新处理了来自Camelyon-16和Camelyon-17数据集的1,399张WSIs及其标签，移除了低质量切片，纠正了错误标签，并为此前未发布的测试集中的肿瘤区域提供了专家级像素标注。基于重新标注的肿瘤区域大小，我们将二分类癌症筛查任务升级为四分类任务：阴性、微转移、宏转移和孤立肿瘤细胞（ITC）。我们使用清理后的数据集重新评估了预训练病理学特征提取器和多示例学习（MIL）方法，提供了一个推动组织病理学AI发展的基准。
英文摘要：
Advances in optical microscopy scanning have significantly contributed to computational pathology (CPath) by converting traditional histopathological slides into whole slide images (WSIs). This development enables comprehensive digital reviews by pathologists and accelerates AI-driven diagnostic support for WSI analysis. Recent advances in foundational pathology models have increased the need for benchmarking tasks. The Camelyon series is one of the most widely used open-source datasets in computational pathology. However, the quality, accessibility, and clinical relevance of the labels have not been comprehensively evaluated. In this study, we reprocessed 1,399 WSIs and labels from the Camelyon-16 and Camelyon-17 datasets, removing low-quality slides, correcting erroneous labels, and providing expert pixel annotations for tumor regions in the previously unreleased test set. Based on the sizes of re-annotated tumor regions, we upgraded the binary cancer screening task to a four-class task: negative, micro-metastasis, macro-metastasis, and Isolated Tumor Cells (ITC). We reevaluated pre-trained pathology feature extractors and multiple instance learning (MIL) methods using the cleaned dataset, providing a benchmark that advances AI development in histopathology.
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