---
license: mit
task_categories:
  - image-classification
language:
  - en
tags:
  - medical-imaging
  - chest-xray
  - healthcare
pretty_name: NIH Chest X-ray14
size_categories:
  - 10K<n<100K
---

# NIH Chest X-ray14 Dataset

## Dataset Description
This dataset contains 112120 chest X-ray images with multiple disease labels per image.

### Labels
Atelectasis, Cardiomegaly, Consolidation, Edema, Effusion, Emphysema, Fibrosis, Hernia, Infiltration, Mass, No Finding, Nodule, Pleural_Thickening, Pneumonia, Pneumothorax

## Dataset Structure
- Train split: 78484 images
- Validation split: 16818 images
- Test split: 16818 images

## Data Format
This dataset is optimized for streaming. Instead of containing the actual images, it contains:
- `image_path`: Path to the image file
- `labels`: List of disease labels for the image

## Loading Images
To load images during training, use a loading function:

```python
from datasets import load_dataset
import torchvision.transforms as T

# Define image loading and transforms
def load_and_transform_image(example):
    image = Image.open(example['image_path']).convert('RGB')
    transform = T.Compose([
        T.Resize((224, 224)),
        T.ToTensor(),
        T.Normalize(mean=[0.485, 0.456, 0.406],
                   std=[0.229, 0.224, 0.225])
    ])
    example['image'] = transform(image)
    return example

# Load dataset with streaming
dataset = load_dataset("path/to/dataset")

# Set up data loading
dataset = dataset.map(
    load_and_transform_image,
    remove_columns=['image_path']
)
```

## Memory Usage
This dataset uses a streaming approach where images are loaded on-the-fly during training,
making it memory efficient and suitable for large-scale training.