---
title: SPIDER-skin
canonical_url: "https://www.modelscope.cn/datasets/histai/SPIDER-skin"
md_url: "https://www.modelscope.cn/datasets/histai/SPIDER-skin.md"
repository: histai/SPIDER-skin
last_updated: 2025-05-15
license: "Apache License 2.0"
storage_size: "237 GB"
downloads: 694
stars: 1
---

# SPIDER-skin

> SPIDER-skin - histai 在 ModelScope 开源的数据集。SPIDER is a collection of supervised pathological datasets covering multiple organs, each with comprehensive class coverage. These datasets are professionally annotated by pathologists.

histai/SPIDER-skin 是 ModelScope 魔搭社区上的数据集，存储大小 237 GB，采用 Apache License 2.0 许可。

- **Repository**: histai/SPIDER-skin
- **License**: Apache License 2.0
- **Storage size**: 237 GB
- **Downloads**: 694
- **Stars**: 1
- **Last updated**: 2025-05-15

Source: https://www.modelscope.cn/datasets/histai/SPIDER-skin

---

# 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 models@hist.ai.

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:** dmitry@hist.ai, alex@hist.ai, kate@hist.ai