清理并升级乳腺癌淋巴结转移数据集，推动病理AI精准诊断
Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis in Breast Cancer Sections
- 重处理1399张全切片图像，修正错误标签，提供专家级像素标注
- 将二分类任务升级为四类：阴性、微转移、大转移、孤立肿瘤细胞
- 为病理图像分析提供更可靠基准，助力AI模型训练与评估
光学显微镜扫描技术的进步使传统组织病理切片转化为全切片图像（WSI），推动计算病理学发展，支持病理医生全面数字阅片，并加速基于AI的WSI分析诊断。近年来基础病理模型进展加快，亟需可靠的评测基准。目前广泛使用的Camelyon系列数据集在标签质量、可访问性和临床相关性方面尚未全面评估。本研究重新处理了来自Camelyon-16和Camelyon-17的1,399张全切片图像及对应标签，剔除低质量切片，修正错误标注，并为此前未公开的测试集提供专家级肿瘤区域像素标注。根据重新标注的肿瘤区域大小，将原有的二分类癌症筛查任务升级为四分类任务：阴性、微转移、大转移和孤立肿瘤细胞（ITC）。基于清洗后的数据集，重新评估了预训练病理特征提取器及多种多实例学习（MIL）方法，构建了一个更高质量的基准，推动组织病理学中AI的发展。
原文摘要 · Abstract (English)
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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