DOI: 10.5281/zenodo.17624208
title: Pancreas diabetes - wistar rats

description: # Pancreas-diabetes Dataset — Scientific Description

## 1. Overview

The **Pancreas-diabetes** dataset is a curated collection of microscopic histological images of Wistar rats pancreatic tissue obtained at 100× magnification. Wistar Glucose intolerant rat females were compared with standard Wistar females with normal glucose tolerance (control). It is designed for supervised learning experiments in medical image analysis, with a focus on distinguishing normal tissue morphology from early signs of glucose intolerance. The dataset contains **90 RGB images**, each with a resolution of **1280 × 960 pixels**, divided into:

- **58 images of normal pancreatic tissue**- **32 images of glucose-intolerant tissue**

The dataset is intended for machine learning research, biomedical image classification, low-data learning studies, and educational use. It is released under the **Creative Commons Attribution 4.0 International (CC-BY-4.0)** license.

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## 2. Scientific Motivation

Pancreatic tissue morphology provides valuable insight into metabolic function and early pathological alterations associated with glucose intolerance and diabetes-related processes. Microscopy imaging, combined with machine learning, enables the identification of structural differences that may not be easily perceptible by manual inspection.

This dataset supports research in:

- biomedical image classification  - early detection of metabolic dysregulation  - analysis of tissue architecture using image-based features  - transfer learning and few-shot learning in histology  - evaluation of machine learning algorithms under limited sample size  - explainable AI (XAI) applied to microscopy images  

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## 3. Image Acquisition and Conditions

All images were acquired under controlled laboratory conditions with the following characteristics:

- **Magnification:** 100×  - **Microscopy:** Optical bright-field microscopy  - **Image resolution:** 1280 × 960 pixels  - **Color format:** RGB  - **File formats:** JPEG or PNG  - **Preparation:** Standard histological staining - **Digitization:** Camera-coupled microscope imaging system  

Each image contains a representative field of pancreatic tissue, including visible acinar structures, ducts, and surrounding morphology. Minor variations in illumination or texture reflect natural biological and technical variability.

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## 4. Class Definition

The dataset defines a **binary classification task**:

| Class | Meaning | Number of Images ||--------------------|----------------------------------------------------------------------------------|-----|| `normal`       | Healthy/normal Wistar rats pancreatic tissue                | 58 || `intolerant` | Pancreatic tissue from glucose-intolerant Wistar rats | 32 |

 

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## 5. File Structure

The dataset is organized as follows:

```Pancreas-diabetes/├── images/│   ├── normal/│   └── intolerant/├── labels.csv├── DESCRIPTION.md├── README.md└── LICENSE```

### 5.1 labels.csv

The accompanying `labels.csv` file contains two columns:

```filepath,classnormal/img001.jpg,normal...intolerant/img032.jpg,intolerant```

This file defines image-level annotations, enabling immediate integration with machine learning pipelines.

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## 6. Intended Use and Limitations

### Suitable for:- Benchmarking of supervised learning algorithms  - CNNs and transfer learning experiments  - Data augmentation research  - Studies of low-data generalization  - XAI/interpretability methods  - Academic projects and coursework  

### Not suitable for:- Clinical diagnosis  - Human/clinical patient-level inference  - Automated medical decision-making  

This dataset is experimental and provided solely for research and educational purposes.

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## 7. Ethical and Legal Considerations

- No personal or identifiable human data are included.  - Samples originate from controlled laboratory experiments with non-human biological tissue.  - The dataset complies with GDPR and related regulations.  - Released under **CC-BY-4.0**, permitting reuse with appropriate attribution.

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## 8. Authors and Institution

- **Luís Rato**  - **F.C. Silva**  - **A.R. Costa**  - **C. Antunes**

**University of Évora, Portugal**

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## 9. Citation

Please cite this dataset as:

> Rato L., Silva F.C., Costa A.R., Antunes C. (2025). *Pancreas-diabetes: Histology Image Dataset for Binary Classification* (Version 1.0). University of Évora. Released under CC-BY-4.0.

> Rato, L., Silva, F.C., Costa, A.R., & Antunes, C.M. (2013). *Analysis of pancreas histological images for glucose intolerance identificationusing ImageJ—preliminary results*, Computational Vision and Medical Image Processing IV, VIPIMAGE 2013. CRC Press 2013, pp: 319–322.---

## 10. Version and Availability

- **Version:** 1.0  - **Release year:** 2025  - **License:** CC-BY-4.0  - **Permanent archive:** Zenodo DOI (to be added)  - **Machine-learning-ready version:** OpenML dataset (to be added)

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