Dominic586/DominicSmart

---
license: mit
task_categories:
- image-classification
language:
- en
tags:
- biology
- medical
pretty_name: HEp-2 Cell
size_categories:
- 10K<n<100K
---

# Dataset card for HEp2
The HEp-2 (Human Epithelial type 2) dataset is a widely used benchmark in the field of medical image analysis, especially for the task of antinuclear antibody (ANA) pattern classification. The dataset contains microscopic images of HEp-2 cells stained with fluorescence, demonstrating multiple patterns of autoantibody binding associated with various autoimmune diseases. The HEp-2 dataset is utilized by researchers and practitioners to develop and evaluate algorithms for automated ANA pattern recognition to aid in the diagnosis of autoimmune diseases. The intricate patterns in this dataset test the robustness of computational models, making it a valuable resource for advancing the understanding of autoimmune diseases and the development of advanced medical image analysis techniques.

## Usage
```python
from datasets import load_dataset

ds = load_dataset("Genius-Society/HEp2", split="train")
labels = ds.features["label"].names
for item in ds:
print("image: ", item["image"])
print("label name: " + labels[item["label"]])
```

## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone [e-mail]:datasets/Genius-Society/HEp2
cd HEp2
```

## Mirror
<https://www.modelscope.cn/datasets/Genius-Society/HEp2>

## Thanks
- [Chapter III ‐ Classifying Cell Images Using Deep Learning Models](https://github.com/Genius-Society/medical_image_computing/blob/hep2/README.md)
- <a href="https://arxiv.org/pdf/1504.02531v1.pdf">HEp-2 Cell Image Classification with Deep Convolutional Neural Networks</a>

许可证：MIT协议
任务类别：图像分类
语言：英语
标签：生物学、医学
美观名称：HEp-2细胞
样本规模：10000 < 样本数 < 100000

# HEp-2 数据集卡片
HEp-2（人上皮细胞2型，Human Epithelial type 2）数据集是医学图像分析领域广泛使用的基准数据集，尤其适用于抗核抗体（antinuclear antibody, ANA）模式分类任务。该数据集包含经荧光染色的HEp-2细胞显微图像，呈现出与多种自身免疫疾病相关的多种自身抗体结合模式。研究人员与从业者常利用HEp-2数据集开发并评估自动化ANA模式识别算法，以辅助自身免疫疾病的诊断。本数据集内的复杂图像模式可用于检验计算模型的鲁棒性，是推动自身免疫疾病研究、进阶医学图像分析技术发展的宝贵资源。

## 使用方法
python
from datasets import load_dataset

ds = load_dataset("Genius-Society/HEp2", split="train")
labels = ds.features["label"].names
for item in ds:
print("图像：", item["image"])
print("标签名称：" + labels[item["label"]])

## 维护方式
bash
GIT_LFS_SKIP_SMUDGE=1 git clone [e-mail]:datasets/Genius-Society/HEp2
cd HEp2

## 镜像地址
<https://www.modelscope.cn/datasets/Genius-Society/HEp2>

## 致谢
- [第三章：基于深度学习模型的细胞图像分类](https://github.com/Genius-Society/medical_image_computing/blob/hep2/README.md)
- <a href="https://arxiv.org/pdf/1504.02531v1.pdf">基于深度卷积神经网络的HEp-2细胞图像分类</a>