构建多中心乳腺细针穿刺细胞学数据集，支持病理切片级智能分类。
A Multi Center Breast FNAC Whole-Slide Cytology Dataset for AI-Assisted Patch-Wise Classification Using C1 to C5 Reporting Categories
- 基于真实临床数据，按C1-C5分级标注7398张病理切片块。
- 覆盖470张全幻灯片，含446张带标注区域的图像，来自印度多家三甲医院。
- 提供完整数据集、标注文件与代码，适合医学AI研究者使用。
本文发布一个用于人工智能辅助分块分类的多中心乳腺细针穿刺细胞学（FNAC）数据集，采用C1至C5报告分级体系。该前瞻性数据集包含321名患者及470张全幻灯片图像（WSIs），采集于2023年5月至2026年3月期间印度多家三级医疗机构。滑片分别采用巴氏染色（190张）或马氏-格鲁恩瓦尔德吉姆萨染色（280张），在哈马姆茨NanoZoomer S360扫描仪上以40倍放大倍率、每像素0.25微米分辨率扫描，原始存储为NDPI格式。470张全幻灯片中，446张包含专家标注的切片区域，共生成7,398张带有专家验证的C1–C5标签的PNG图像块。数据集发布内容包括：原始NDPI全幻灯片、逐级地理标注的GeoJSON文件、提取出的图像块、去标识化元数据、数据字典、验证摘要、链接全幻灯片与Zenodo记录的清单文件，以及用于数据检查与重用的代码。数据集总容量约950 GB，可通过Zenodo获取。
原文摘要 · Abstract (English)
We present a multi center breast fine needle aspiration cytology (FNAC) dataset designed for patch wise classification using C1 to C5 reporting labels. The prospective dataset includes 321 patients and 470 whole-slide images (WSIs) collected from participating tertiary medical centers in India between May 2023 and March 2026. Slides were stained using Papanicolaou (190 WSIs) or MayGrunwald Giemsa (280 WSIs), scanned on a Hamamatsu NanoZoomer S360 at 40X magnification and 0.25 microns per pixel, and stored directly in NDPI format. Across the 470 WSIs, 446 WSIs contain annotated patch regions, yielding 7,398 PNG image patches with expert-verified C1 to C5 labels. The release includes NDPI WSIs, WSI-level GeoJSON annotation files, extracted patch images, deidentified metadata, a data dictionary, a validation summary, a manifest linking WSIs to Zenodo records, and code for dataset inspection and reuse. The complete dataset is approximately 950 GB and is available through Zenodo.
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