🤖 AI Summary
Current histopathological image datasets for colorectal cancer generally lack comprehensive coverage of all WHO differentiation grades and multi-magnification registered samples, limiting the application of AI in automated cancer grading. This study addresses this gap by constructing a high-quality, publicly available dataset comprising 1,914 H&E-stained images from 214 colorectal adenocarcinoma patients, encompassing well-, moderately, and poorly differentiated tumors. For each patient, precisely registered images across four magnifications (4× to 40×) are provided. Annotated according to WHO standards and curated through standardized acquisition and release protocols, the dataset is openly shared via Mendeley Data and databiox.com. It represents the first resource to simultaneously offer multi-grade, multi-magnification, and patient-level registered pathological images for colorectal cancer, substantially enhancing the comprehensiveness and reliability of AI model training and evaluation.
📝 Abstract
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.