Dataset and AI Workflow for Deep Learning Image Classification of Ulcerative Colitis and Colorectal Cancer

Joaquim Carreras (), Giovanna Roncador and Rifat Hamoudi
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Joaquim Carreras: Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, Japan
Giovanna Roncador: Monoclonal Antibodies Unit, Spanish National Cancer Research Center (CNIO), Melchor Fernandez Almagro 3, 28029 Madrid, Spain
Rifat Hamoudi: Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates

Data, 2025, vol. 10, issue 7, 1-30

Abstract: Inflammatory bowel disease (IBD) is a chronic inflammatory condition of the gastrointestinal tract characterized by the deregulation of immuno-oncology markers. IBD includes ulcerative colitis and Crohn’s disease. Chronic active inflammation is a risk factor for the development of colorectal cancer (CRC). This technical note describes a dataset of histological images of ulcerative colitis, CRC (adenocarcinoma), and colon control. The samples were stained with hematoxylin and eosin (H&E), and immunohistochemically analyzed for LAIR1 and TOX2 markers. The methods used for collecting, processing, and analyzing scientific data, including this dataset, using convolutional neural networks (CNNs) and information about the dataset’s use are also described. This article is a companion to the manuscript “Ulcerative Colitis, LAIR1 and TOX2 Expression, and Colorectal Cancer Deep Learning Image Classification Using Convolutional Neural Networks”.

Keywords: ulcerative colitis; colorectal cancer; adenocarcinoma; artificial intelligence; deep learning; convolutional neural network; transfer learning; TOX2; LAIR1; H&E; whole-slide images; ResNet-18 (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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