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

• URL: http://arxiv.org/abs/2607.12750v1
• Date: Tue, 14 Jul 2026 13:18:20 GMT
• Title: CRC-HGD: A Histopathological Image Dataset for Grading Colorectal Cancer
• Abstract summary: Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related deaths globally.<n> Accurate histologic grading plays a critical role in prognosis and treatment planning for colorectal adenocarcinoma.<n>In recent years, artificial intelligence and its subcategories, including machine learning and deep learning, have been increasingly employed for automated cancer detection and classification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Semi-Supervised Learning-Based Genetic Biomarkers Dataset for Multiple-Stage Hepatocellular Carcinoma Prediction [2.2251452674183922]
  The most common form of liver cancer is carcinoma (HCC), accounting for over 90% of liver cancer cases.<n>There is a distinct lack of publicly available HCC datasets utilizing genomic data.<n>This study proposes constructing a multi-stage HCC dataset using XGBoost and Semi-Supervised learning on three separate datasets of genomic biomarkers.
  arXiv  Detail & Related papers  (2026-09-15T12:33:12Z)
• A Pathology Foundation Model for Gastric Cancer with Real-World Validation [47.19073124722865]
  We present GRACE, a Gastric-specific foundation model for Real-world Assessment and Clinical dEcision support.<n> GRACE was developed from multicenter gastric pathology datasets totaling 48,364 primarily HE-stained whole-slide images from 37,493 patients.<n>When calibrated to maintain non-inferior performance to senior pathologists, the AI-assisted workflow could triage 60.7% of atrophy and 82.7% of intestinal metaplasia cases.
  arXiv  Detail & Related papers  (2026-06-03T12:18:06Z)
• Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge [12.259796670328015]
  Colorectal cancer (CRC) is the third most diagnosed cancer and the second leading cause of cancer-related death worldwide.<n>To promote automated and standardized solutions, we organized the ICIP Grand Challenge on Colorectal Cancer Tumor Grading.<n>The dataset comprises 103 whole-slide images with expert pixel-level annotations for five tissue classes.
  arXiv  Detail & Related papers  (2025-07-07T05:59:16Z)
• CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray [64.2434525370243]
  The CXR-LT series is a community-driven initiative designed to enhance lung disease classification using chest X-rays.<n>The CXR-LT 2024 expands the dataset to 377,110 chest X-rays (CXRs) and 45 disease labels, including 19 new rare disease findings.<n>This paper provides an overview of CXR-LT 2024, detailing the data curation process and consolidating state-of-the-art solutions.
  arXiv  Detail & Related papers  (2025-06-09T17:53:31Z)
• Leveraging Sparse Annotations for Leukemia Diagnosis on the Large Leukemia Dataset [44.948939549346676]
  Leukemia is the 10th most frequently diagnosed cancer and one of the leading causes of cancer-related deaths worldwide.<n>Despite deep learning advances in medical imaging, leukemia analysis lacks a large, diverse multi-task dataset.<n>We present a large-scale WBC dataset and novel methods for detecting WBC with their attributes.
  arXiv  Detail & Related papers  (2025-04-03T14:04:02Z)
• MRANet: A Modified Residual Attention Networks for Lung and Colon Cancer Classification [0.0]
  Lung and colon cancers are predominant contributors to cancer mortality.<n>By utilizing imaging technology in different image detection, learning models have shown promise in automating cancer classification.<n>We proposed a novel approach based on a modified residual attention network architecture.<n>Our proposed model achieved an exceptional accuracy of 99.30%, 96.63%, and 97.56% for two, three, and five classes, respectively.
  arXiv  Detail & Related papers  (2024-12-23T16:31:45Z)
• A Knowledge-enhanced Pathology Vision-language Foundation Model for Cancer Diagnosis [58.85247337449624]
  We propose a knowledge-enhanced vision-language pre-training approach that integrates disease knowledge into the alignment within hierarchical semantic groups.<n>KEEP achieves state-of-the-art performance in zero-shot cancer diagnostic tasks.
  arXiv  Detail & Related papers  (2024-12-17T17:45:21Z)
• Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge [15.649976310277099]
  Organ and cancer segmentation in abdomen Computed Tomography (CT) scans is the prerequisite for precise cancer diagnosis and treatment. Most existing benchmarks and algorithms are tailored to specific cancer types, limiting their ability to provide comprehensive cancer analysis. This work presents the first international competition on abdominal organ and pan-cancer segmentation by providing a large-scale and diverse dataset.
  arXiv  Detail & Related papers  (2024-08-22T16:38:45Z)
• COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics [69.55060769611916]
  The use of chest X-ray (CXR) imaging as a complementary screening strategy to RT-PCR testing is increasing. Many visual perception models have been proposed for COVID-19 screening based on CXR imaging. We introduce COVIDx CXR-3, a large-scale benchmark dataset of CXR images for supporting COVID-19 computer vision research.
  arXiv  Detail & Related papers  (2022-06-08T04:39:44Z)
• Topological Data Analysis of copy number alterations in cancer [70.85487611525896]
  We explore the potential to capture information contained in cancer genomic information using a novel topology-based approach. We find that this technique has the potential to extract meaningful low-dimensional representations in cancer somatic genetic data.
  arXiv  Detail & Related papers  (2020-11-22T17:31:23Z)
• Hierarchical Deep Learning Ensemble to Automate the Classification of Breast Cancer Pathology Reports by ICD-O Topography [0.0]
  We present a hierarchical deep learning ensemble method incorporating state of the art convolutional neural network models for the automatic labelling of 2201 pathology reports. Our results show an improvement in primary site classification over the state of the art CNN model by greater than 14% for F1 micro and 55% for F1 macro scores.
  arXiv  Detail & Related papers  (2020-08-28T10:29:56Z)
• Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection [49.32653090178743]
  Early detection of head and neck tumors is crucial for patient survival. Hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors. We present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI.
  arXiv  Detail & Related papers  (2020-04-21T17:07:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.