CRC-HGD：用于结直肠癌分级的组织病理学图像数据集
CRC-HGD: A Histopathological Image Dataset for Grading Colorectal Cancer
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中文总结 AI 辅助
介绍用于结直肠癌分级的CRC-HGD数据集，该数据集含1914张来自214名患者的图像，有四个放大级别，涵盖三个分化等级，可通过特定网址获取，能助力结直肠癌分级的全面计算分析。
中文摘要 AI 辅助
结直肠癌是全球第三大常见癌症，也是癌症相关死亡的第二大主要原因。准确的组织学分级对结直肠癌的预后和治疗规划至关重要。近年来，人工智能及其子类别被越来越多地用于癌症自动检测和分类。本文介绍了CRC-HGD，这是一个包含1914张图像的组织病理学显微镜图像数据集，来自214名结直肠癌患者（I级106例、II级75例、III级33例）。标本是在伊朗伊斯法罕医科大学普尔西纳·哈基姆研究中心采集的H&E染色结直肠组织切片，于2014年至2019年诊断，并根据世界卫生组织标准分为三个等级。每个标本提供4倍、10倍、20倍和40倍四个放大级别。该数据集可通过Mendeley Data获取，其独特之处在于提供了多放大级别下所有三个分化等级的标记标本，便于对结直肠癌分级进行全面计算分析。
英文摘要
Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related deaths globally, with approximately 1,926,425 new cases and 904,019 deaths reported in 2022. Accurate histologic grading plays a critical role in prognosis and treatment planning for colorectal adenocarcinoma. In recent years, artificial intelligence and its subcategories, including machine learning and deep learning, have been increasingly employed for automated cancer detection and classification. An appropriate and well-organized dataset is the essential first step to achieve this goal. This paper introduces CRC-HGD, a histopathological microscopy image dataset of 1,914 images obtained from 214 colorectal adenocarcinoma patients (Grade I: 106, Grade II: 75, Grade III: 33). The specimens are H&E-stained colorectal tissue sections acquired at the Poursina Hakim Research Center of Isfahan University of Medical Sciences, Iran, diagnosed between 2014 and 2019, and graded according to the World Health Organization (WHO) criteria into three grades: well-differentiated (Grade I), moderately differentiated (Grade II), and poorly differentiated (Grade III). For each specimen, four magnification levels are provided: 4x, 10x, 20x, and 40x. The dataset is accessible via Mendeley Data (https://doi.org/10.17632/yfp5sfj47m.4) and at http://databiox.com, where the latest version is also available. The distinctive feature of this dataset is the provision of labeled specimens across all three differentiation grades at multiple magnification levels, enabling comprehensive computational analysis of colorectal cancer grading.
