
# Dataset card for "MuGeminorum/HEp2"
The HEp-2 (Human Epithelial type 2) dataset is a widely utilized benchmark in the field of medical image analysis, particularly for the task of antinuclear antibody (ANA) pattern classification. This dataset comprises microscopic images of HEp-2 cells stained with fluorescent dyes, showcasing diverse patterns of autoantibody binding associated with various autoimmune diseases. Researchers and practitioners leverage the HEp-2 dataset to develop and assess algorithms for automating ANA pattern recognition, thereby aiding in the diagnosis of autoimmune disorders. The intricate patterns within the dataset challenge the robustness of computational models, making it a valuable resource for advancing the understanding of autoimmune diseases and contributing to the development of cutting-edge medical image analysis techniques.

## Usage
```python
from datasets import load_dataset

data = load_dataset("MuGeminorum/HEp2")
trainset = data["train"]
validset = data["validation"]
testset = data["test"]
labels = testset.features["label"].names

for item in trainset:
    print("image: ", item["image"])
    print("label name: " + labels[item["label"]])

for item in validset:
    print("image: ", item["image"])
    print("label name: " + labels[item["label"]])

for item in testset:
    print("image: ", item["image"])
    print("label name: " + labels[item["label"]])
```

## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/MuGeminorum/HEp2
```

## Mirror
<https://www.modelscope.cn/datasets/MuGeminorum/HEp2>

## Reference
[1] [Chapter III ‐ Classifying Cell Images Using Deep Learning Models](https://github.com/MuGeminorum/Medical_Image_Computing/wiki/Chapter-III-%E2%80%90-Classifying-Cell-Images-Using-Deep-Learning-Models)<br>
[2] <a href="https://arxiv.org/pdf/1504.02531v1.pdf">HEp-2 Cell Image Classification with Deep Convolutional Neural Networks</a>
task_categories:image-classification size_categories:10K<n<100K language:en license:mit biology medical arxiv:1504.02531 region:us
{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["image-classification"], "pretty_name": "HEp-2 Cell", "tags": ["biology", "medical"]}
TAGS
#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #biology #medical #arxiv-1504.02531 #region-us 


# Dataset card for "MuGeminorum/HEp2"
The HEp-2 (Human Epithelial type 2) dataset is a widely utilized benchmark in the field of medical image analysis, particularly for the task of antinuclear antibody (ANA) pattern classification. This dataset comprises microscopic images of HEp-2 cells stained with fluorescent dyes, showcasing diverse patterns of autoantibody binding associated with various autoimmune diseases. Researchers and practitioners leverage the HEp-2 dataset to develop and assess algorithms for automating ANA pattern recognition, thereby aiding in the diagnosis of autoimmune disorders. The intricate patterns within the dataset challenge the robustness of computational models, making it a valuable resource for advancing the understanding of autoimmune diseases and contributing to the development of cutting-edge medical image analysis techniques.

## Usage


## Maintenance


## Mirror
<URL

## Reference
[1] Chapter III ‐ Classifying Cell Images Using Deep Learning Models<br>
[2] <a href="URL Cell Image Classification with Deep Convolutional Neural Networks</a>
# Dataset card for "MuGeminorum/HEp2"
The HEp-2 (Human Epithelial type 2) dataset is a widely utilized benchmark in the field of medical image analysis, particularly for the task of antinuclear antibody (ANA) pattern classification. This dataset comprises microscopic images of HEp-2 cells stained with fluorescent dyes, showcasing diverse patterns of autoantibody binding associated with various autoimmune diseases. Researchers and practitioners leverage the HEp-2 dataset to develop and assess algorithms for automating ANA pattern recognition, thereby aiding in the diagnosis of autoimmune disorders. The intricate patterns within the dataset challenge the robustness of computational models, making it a valuable resource for advancing the understanding of autoimmune diseases and contributing to the development of cutting-edge medical image analysis techniques. ## Usage ## Maintenance ## Mirror
<URL ## Reference
[1] Chapter III ‐ Classifying Cell Images Using Deep Learning Models<br>
[2] <a href="URL Cell Image Classification with Deep Convolutional Neural Networks</a>
TAGS
#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #biology #medical #arxiv-1504.02531 #region-us 
 # Dataset card for "MuGeminorum/HEp2"
The HEp-2 (Human Epithelial type 2) dataset is a widely utilized benchmark in the field of medical image analysis, particularly for the task of antinuclear antibody (ANA) pattern classification. This dataset comprises microscopic images of HEp-2 cells stained with fluorescent dyes, showcasing diverse patterns of autoantibody binding associated with various autoimmune diseases. Researchers and practitioners leverage the HEp-2 dataset to develop and assess algorithms for automating ANA pattern recognition, thereby aiding in the diagnosis of autoimmune disorders. The intricate patterns within the dataset challenge the robustness of computational models, making it a valuable resource for advancing the understanding of autoimmune diseases and contributing to the development of cutting-edge medical image analysis techniques. ## Usage ## Maintenance ## Mirror
<URL ## Reference
[1] Chapter III ‐ Classifying Cell Images Using Deep Learning Models<br>
[2] <a href="URL Cell Image Classification with Deep Convolutional Neural Networks</a>
passage: TAGS
#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #biology #medical #arxiv-1504.02531 #region-us 
# Dataset card for "MuGeminorum/HEp2"
The HEp-2 (Human Epithelial type 2) dataset is a widely utilized benchmark in the field of medical image analysis, particularly for the task of antinuclear antibody (ANA) pattern classification. This dataset comprises microscopic images of HEp-2 cells stained with fluorescent dyes, showcasing diverse patterns of autoantibody binding associated with various autoimmune diseases. Researchers and practitioners leverage the HEp-2 dataset to develop and assess algorithms for automating ANA pattern recognition, thereby aiding in the diagnosis of autoimmune disorders. The intricate patterns within the dataset challenge the robustness of computational models, making it a valuable resource for advancing the understanding of autoimmune diseases and contributing to the development of cutting-edge medical image analysis techniques.## Usage## Maintenance## Mirror
<URL## Reference
[1] Chapter III ‐ Classifying Cell Images Using Deep Learning Models<br>
[2] <a href="URL Cell Image Classification with Deep Convolutional Neural Networks</a>