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 [e-mail].

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:** [e-mail], [e-mail], [e-mail]

# SPIDER-SKIN 数据集

SPIDER 是一套覆盖多器官的有监督病理数据集集合，各类别覆盖全面。所有数据集均由病理学家完成专业标注。

若您希望支持、赞助或获取 SPIDER 数据与模型的商业授权，请联系我们：[e-mail]。

如需了解 SPIDER 的详细说明、研究方法与基准测试结果，请参阅我们的研究论文：
**SPIDER：一款全面的多器官有监督病理数据集与基准模型**
[在arXiv上查看](https://arxiv.org/abs/2503.02876)

本仓库包含 **SPIDER-skin** 数据集。如需探索其他器官的数据集，请访问 [Hugging Face HistAI 页面](https://huggingface.co/histai) 或 [GitHub 仓库](https://github.com/HistAI/SPIDER)。SPIDER 会定期更新新增器官与数据，欢迎关注 Hugging Face 以获取最新动态。

---

### 概述
SPIDER-skin 是面向皮肤器官的图像-标签对有监督数据集。每条数据包含：
- 一张**中心224×224像素图像块**及其类别标签
- **24张相同尺寸的周边上下文图像块**，共同组成**1120×1120像素的复合区域**
- 所有图像块均以**20倍放大倍率**提取

我们提供了**训练-测试划分**以保证基准测试的一致性。该划分基于**玻片级别**完成，确保同一张全视野数字切片（Whole Slide Image，WSI）的图像块不会同时出现在训练集与测试集中。用户也可根据需求自行合并或重新划分数据。

## 如何使用

### 下载数据集
#### 方案1：使用 `huggingface_hub`
python
from huggingface_hub import snapshot_download

snapshot_download(repo_id="histai/SPIDER-skin", repo_type="dataset", local_dir="/local_path")

#### 方案2：使用 `git`
bash
# 确保已安装 Git LFS（https://git-lfs.com）
git lfs install
git clone https://huggingface.co/datasets/histai/SPIDER-skin

### 解压数据集
本数据集以多个tar压缩包形式提供，请使用以下命令解压：
bash
cat spider-skin.tar.* | tar -xvf -

### 使用数据集
解压后，您将得到：
- 一个 `images/` 文件夹
- 一个 `metadata.json` 元数据文件

您可以通过两种方式处理并使用该数据集：
#### 1. 直接在代码中使用（推荐用于PyTorch训练）
使用 `scripts/spider_dataset.py` 中提供的数据集类。该类接收以下参数：
- 数据集路径（包含 `metadata.json` 与 `images/` 文件夹的根目录）
- 上下文尺寸：`5`、`3` 或 `1`
- `5`：完整的**1120×1120**图像块（默认值）
- `3`：**672×672**图像块
- `1`：仅保留中心图像块

该数据集类会动态返回拼接后的图像，适配直接集成到PyTorch训练流程中。
#### 2. 转换为ImageNet格式
如需将数据集结构化以适配标准工具，请使用 `scripts/convert_to_imagenet.py` 脚本进行转换。该脚本同样支持不同的上下文尺寸。

转换后将生成如下目录结构：

<output_dir>/<split>/<class>/<slide>/<image>

您可以通过以下方式加载使用：
python
from datasets import load_dataset

dataset = load_dataset("imagefolder", data_dir="/path/to/folder")

或使用
`torchvision.datasets.ImageFolder` 类

---

### 数据集构成
SPIDER-skin 数据集包含以下类别：

| 类别名称 | 中心图像块数量 |
|------------------------------|------------|
| 光化性角化病 | 4936 |
| 顶泌汗腺 | 6739 |
| 基底细胞癌 | 6446 |
| 原位癌 | 5478 |
| 胶原组织 | 6262 |
| 表皮组织 | 7449 |
| 脂肪组织 | 6525 |
| 毛囊结构 | 8343 |
| 炎症组织 | 5856 |
| 侵袭性黑色素瘤 | 9101 |
| 卡波西肉瘤 | 4778 |
| 角蛋白 | 6418 |
| 原位黑色素瘤 | 4545 |
| 默克尔细胞癌 | 5968 |
| 肌肉组织 | 6051 |
| 坏死组织 | 6842 |
| 神经组织 | 4735 |
| 色素痣 | 8937 |
| 皮脂腺 | 6639 |
| 脂溢性角化病 | 10311 |
| 日光性弹性组织变性 | 7613 |
| 鳞状细胞癌 | 6051 |
| 血管组织 | 7673 |
| 疣状病变 | 6158 |

**总样本统计：**
- **159,854** 张中心图像块
- **2,696,987** 张总图像块（含上下文图像块）
- 共计**3,784** 张用于标注的全视野数字切片

---

## 授权协议
本数据集采用 **CC BY-NC 4.0** 协议授权，仅可用于**科研用途**。

## 引用说明
若您在研究工作中使用本数据集，请引用以下文献：
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},
}

## 联系方式
- **作者团队**：Dmitry Nechaev、Alexey Pchelnikov、Ekaterina Ivanova
- **邮箱**：[e-mail]、[e-mail]、[e-mail]