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SPIDER-skin / README.md
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---
license: cc-by-nc-4.0
task_categories:
- image-classification
size_categories:
- 1M<n<10M
---
# SPIDER-SKIN Dataset
SPIDER is a collection of supervised pathological datasets covering multiple organs, each with comprehensive class coverage. These datasets are professionally annotated by pathologists.
If you would like to support, sponsor, or obtain a commercial license for the SPIDER data and models, please contact us at [email protected].
For a detailed description of SPIDER, methodology, and benchmark results, refer to our research paper:
**SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and Baseline Models**
[View on arXiv](https://arxiv.org/abs/2503.02876)
This repository contains the **SPIDER-skin** dataset. To explore datasets for other organs, visit the [Hugging Face HistAI page](https://huggingface.co/histai) or [GitHub](https://github.com/HistAI/SPIDER). SPIDER is regularly updated with new organs and data, so follow us on Hugging Face to stay updated.
---
### Overview
SPIDER-skin is a supervised dataset of image-class pairs for the skin organ. Each data point consists of:
- A **central 224×224 patch** with a class label
- **24 surrounding context patches** of the same size, forming a **composite 1120×1120 region**
- Patches are extracted at **20X magnification**
We provide a **train-test split** for consistent benchmarking. The split is done at the **slide level**, ensuring that patches from the same whole slide image (WSI) do not appear in both training and test sets. Users can also merge and re-split the data as needed.
## How to Use
### Downloading the Dataset
#### Option 1: Using `huggingface_hub`
```python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="histai/SPIDER-skin", repo_type="dataset", local_dir="/local_path")
```
#### Option 2: Using `git`
```bash
# Ensure you have Git LFS installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/datasets/histai/SPIDER-skin
```
### Extracting the Dataset
The dataset is provided in multiple tar archives. Unpack them using:
```bash
cat spider-skin.tar.* | tar -xvf -
```
### Using the Dataset
Once extracted, you will find:
- An `images/` folder
- A `metadata.json` file
You can process and use the dataset in two ways:
#### 1. Directly in Code (Recommended for PyTorch Training)
Use the dataset class provided in `scripts/spider_dataset.py`. This class takes:
- Path to the dataset (folder containing `metadata.json` and `images/` folder)
- Context size: `5`, `3`, or `1`
- `5`: Full **1120×1120** patches (default)
- `3`: **672×672** patches
- `1`: Only central patches
The dataset class dynamically returns stitched images, making it suitable for direct use in PyTorch training pipelines.
#### 2. Convert to ImageNet Format
To structure the dataset for easy use with standard tools, convert it using `scripts/convert_to_imagenet.py`.
The script also supports different context sizes.
This will generate:
```
<output_dir>/<split>/<class>/<slide>/<image>
```
You can then use it with:
```python
from datasets import load_dataset
dataset = load_dataset("imagefolder", data_dir="/path/to/folder")
```
or
`torchvision.datasets.ImageFolder` class
---
### Dataset Composition
The SPIDER-skin dataset consists of the following classes:
| Class | Central Patches |
|--------------------------------|------------|
| Actinic keratosis | 4936 |
| Apocrine glands | 6739 |
| Basal cell carcinoma | 6446 |
| Carcinoma in situ | 5478 |
| Collagen | 6262 |
| Epidermis | 7449 |
| Fat | 6525 |
| Follicle | 8343 |
| Inflammation | 5856 |
| Invasive melanoma | 9101 |
| Kaposi’s sarcoma | 4778 |
| Keratin | 6418 |
| Melanoma in situ | 4545 |
| Mercel cell carcinoma | 5968 |
| Muscle | 6051 |
| Necrosis | 6842 |
| Nerves | 4735 |
| Nevus | 8937 |
| Sebaceous gland | 6639 |
| Seborrheic keratosis | 10311 |
| Solar elastosis | 7613 |
| Squamous cell carcinoma | 6051 |
| Vessels | 7673 |
| Wart | 6158 |
**Total Counts:**
- **159,854** central patches
- **2,696,987** total patches (including context patches)
- **3,784** total slides used for annotation
---
## License
The dataset is licensed under **CC BY-NC 4.0** and is for **research use only**.
## Citation
If you use this dataset in your work, please cite:
```bibtex
@misc{nechaev2025spidercomprehensivemultiorgansupervised,
title={SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and Baseline Models},
author={Dmitry Nechaev and Alexey Pchelnikov and Ekaterina Ivanova},
year={2025},
eprint={2503.02876},
archivePrefix={arXiv},
primaryClass={eess.IV},
url={https://arxiv.org/abs/2503.02876},
}
```
## Contacts
- **Authors:** Dmitry Nechaev, Alexey Pchelnikov, Ekaterina Ivanova
- **Email:** [email protected], [email protected], [email protected]