构建多中心乳腺癌病理图像分割数据集，提升模型泛化能力。
A Multicentric Dataset for Training and Benchmarking Breast Cancer Segmentation in H&E Slides
- 整合三所临床中心与两个公开数据集的587张切片，覆盖所有分子亚型和分级。
- 包含4类标注：浸润性上皮、非浸润性上皮、坏死、其他，重点补充少见形态。
- 支持自动化生物标志物定量研究，适合训练与评测乳腺癌分割模型。
基于人工智能的乳腺癌生物标志物大规模分析依赖于全切片图像（WSI）的自动语义分割，而现有公开数据集在形态多样性方面不足，难以支撑模型泛化与跨患者队列的稳健验证。本文提出乳腺癌组织病理学分割数据集BEETLE，涵盖587例活检与切除标本，来自三家临床中心及两个公开数据集，使用七台扫描仪数字化，覆盖所有分子亚型与组织学分级。通过多样化标注策略，对四类结构——浸润性上皮、非浸润性上皮、坏死、其他——进行标注，特别关注既往数据集中代表性不足的病变形态，如导管原位癌和散在小叶肿瘤细胞。该数据集的多样性及其在自动化生物标志物量化领域的相关性，具备高重用潜力。最后，提供经过精心整理的多中心外部评估集，以实现乳腺癌分割模型的标准化基准测试。
原文摘要 · Abstract (English)
Automated semantic segmentation of whole-slide images (WSIs) stained with hematoxylin and eosin (H&E) is essential for large-scale artificial intelligence-based biomarker analysis in breast cancer. However, existing public datasets for breast cancer segmentation lack the morphological diversity needed to support model generalizability and robust biomarker validation across heterogeneous patient cohorts. We introduce BrEast cancEr hisTopathoLogy sEgmentation (BEETLE), a dataset for multiclass semantic segmentation of H&E-stained breast cancer WSIs. It consists of 587 biopsies and resections from three collaborating clinical centers and two public datasets, digitized using seven scanners, and covers all molecular subtypes and histological grades. Using diverse annotation strategies, we collected annotations across four classes - invasive epithelium, non-invasive epithelium, necrosis, and other - with particular focus on morphologies underrepresented in existing datasets, such as ductal carcinoma in situ and dispersed lobular tumor cells. The dataset's diversity and relevance to the rapidly growing field of automated biomarker quantification in breast cancer ensure its high potential for reuse. Finally, we provide a well-curated, multicentric external evaluation set to enable standardized benchmarking of breast cancer segmentation models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。