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

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”.

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Data
Published
2025-06-24
DOI
https://doi.org/10.3390/data10070099
Primary Topic
Colorectal Cancer Screening and Detection
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Giovanna Roncador, Rifat Hamoudi, Joaquim Carreras
Data
Colorectal Cancer Screening and Detection
article

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

Giovanna Roncador, Rifat Hamoudi, Joaquim Carreras
article en

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”.

DataVol. 10(7)
Abu Dhabi University (AE), Tokai University (JP), University of Sharjah (AE), Spanish National Cancer Research Centre (ES), Centro de Investigación del Cáncer (ES), University College London (GB)
University of Sharjah, Ministry of Education, Culture, Sports, Science and Technology
Openalex Percentile: Top 78%
Colorectal Cancer Screening and Detection
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