CRC-HGD-v1: A Histopathological Image Dataset for Grading Colorectal Cancer

Published: 18 December 2024| Version 2 | DOI: 10.17632/yfp5sfj47m.2
Contributors:
Elham Amjadi, Amin Bahreini, Sayyed Mohammadreza Hakimian, Mohammad Hasan Emami, Alireza Fahim, Hojjatollah Rahimi, Hamidreza Bolhasani

Description

CRC-HGD-v1 is the first version of Colorectal Cancer Histopathological Grading Image Dataset. This dataset includes 1,899 images in 3 categories: 1- Grade I (Well Differentiated), 2- Grade II (Moderately Differentiated), and 3- Grade III (Poorly Differentiated). The first main three category (Grade I, II, and III) consists of images in four magnification level: 4x, 10x, 20x, and 40x. Almost for all of the specimens, there is one image in 4x, three images in 10x, three images in 20x, and three images in 40x, which make it 10 images per specimen. Overall, there is 103 samples for Grade I, 75 samples for Grade II, and 32 samples for Grade III. Considering all images related to four magnifications, there is 860 images in Grade I, 712 in Grade II, and 327 in Grade III. These samples are gathered and prepared by a team of medical experts in pathology, surgery, gastroenterology, artificial intelligence (AI), and data science under supervision of PoursinaHakim Digestive Disease Research Center (PDDRC). For query on the latest update and more information about this dataset, you can refer to: https://databiox.com

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Categories

Artificial Intelligence, Computer Vision, Pathology, Medical Imaging, Colorectal Medicine, Histopathology, Colorectal Cancer, Colorectal Surgery, Convolutional Neural Network, Deep Learning, Image Analysis

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