|
--- |
|
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] |