---

annotations_creators:

- machine-generated

- expert-generated

language_creators:

- machine-generated

- expert-generated

language:

- en

license:

- unknown

multilinguality:

- monolingual

pretty_name: NIH-CXR14

paperswithcode_id: chestx-ray14

size_categories:

- 100K<n<1M

task_categories:

- image-classification

task_ids:

- multi-class-image-classification

---

# Dataset Card for NIH Chest X-ray dataset

## Table of Contents

- [Table of Contents](#table-of-contents)

- [Dataset Description](#dataset-description)

  - [Dataset Summary](#dataset-summary)

  - [Languages](#languages)

- [Dataset Structure](#dataset-structure)

  - [Data Instances](#data-instances)

  - [Data Fields](#data-fields)

  - [Data Splits](#data-splits)

- [Dataset Creation](#dataset-creation)

  - [Curation Rationale](#curation-rationale)

  - [Source Data](#source-data)

  - [Annotations](#annotations)

  - [Personal and Sensitive Information](#personal-and-sensitive-information)

- [Considerations for Using the Data](#considerations-for-using-the-data)

  - [Social Impact of Dataset](#social-impact-of-dataset)

  - [Discussion of Biases](#discussion-of-biases)

  - [Other Known Limitations](#other-known-limitations)

- [Additional Information](#additional-information)

  - [Dataset Curators](#dataset-curators)

  - [Licensing Information](#licensing-information)

  - [Citation Information](#citation-information)

  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [NIH Chest X-ray Dataset of 10 Common Thorax Disease Categories](https://nihcc.app.box.com/v/ChestXray-NIHCC/folder/36938765345)

- **Repository:**

- **Paper:** [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://arxiv.org/abs/1705.02315)

- **Leaderboard:**

- **Point of Contact:** rms@nih.gov

### Dataset Summary

_ChestX-ray dataset comprises 112,120 frontal-view X-ray images of 30,805 unique patients with the text-mined fourteen disease image labels (where each image can have multi-labels), mined from the associated radiological reports using natural language processing. Fourteen common thoracic pathologies include Atelectasis, Consolidation, Infiltration, Pneumothorax, Edema, Emphysema, Fibrosis, Effusion, Pneumonia, Pleural_thickening, Cardiomegaly, Nodule, Mass and Hernia, which is an extension of the 8 common disease patterns listed in our CVPR2017 paper. Note that original radiology reports (associated with these chest x-ray studies) are not meant to be publicly shared for many reasons. The text-mined disease labels are expected to have accuracy >90%.Please find more details and benchmark performance of trained models based on 14 disease labels in our arxiv paper: [1705.02315](https://arxiv.org/abs/1705.02315)_

![](https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/data/nih-chest-xray14-portraint.png)

## Dataset Structure

### Data Instances

A sample from the training set is provided below:

```

{'image_file_path': '/root/.cache/huggingface/datasets/downloads/extracted/95db46f21d556880cf0ecb11d45d5ba0b58fcb113c9a0fff2234eba8f74fe22a/images/00000798_022.png',

 'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=1024x1024 at 0x7F2151B144D0>,

 'labels': [9, 3]}

```

### Data Fields

The data instances have the following fields:

- `image_file_path` a `str` with the image path

- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.

- `labels`: an `int` classification label.

<details>

  <summary>Class Label Mappings</summary>

  ```json

  {

    "No Finding": 0,

    "Atelectasis": 1,

    "Cardiomegaly": 2,

    "Effusion": 3,

    "Infiltration": 4,

    "Mass": 5,

    "Nodule": 6,

    "Pneumonia": 7,

    "Pneumothorax": 8,

    "Consolidation": 9,

    "Edema": 10,

    "Emphysema": 11,

    "Fibrosis": 12,

    "Pleural_Thickening": 13,

    "Hernia": 14

 }

  ```

</details>

**Label distribution on the dataset:**

| labels             |   obs |       freq |

|:-------------------|------:|-----------:|

| No Finding         | 60361 | 0.426468   |

| Infiltration       | 19894 | 0.140557   |

| Effusion           | 13317 | 0.0940885  |

| Atelectasis        | 11559 | 0.0816677  |

| Nodule             |  6331 | 0.0447304  |

| Mass               |  5782 | 0.0408515  |

| Pneumothorax       |  5302 | 0.0374602  |

| Consolidation      |  4667 | 0.0329737  |

| Pleural_Thickening |  3385 | 0.023916   |

| Cardiomegaly       |  2776 | 0.0196132  |

| Emphysema          |  2516 | 0.0177763  |

| Edema              |  2303 | 0.0162714  |

| Fibrosis           |  1686 | 0.0119121  |

| Pneumonia          |  1431 | 0.0101104  |

| Hernia             |   227 | 0.00160382 |

### Data Splits

|             |train| test|

|-------------|----:|----:|

|# of examples|86524|25596|

**Label distribution by dataset split:**

| labels             |   ('Train', 'obs') |   ('Train', 'freq') |   ('Test', 'obs') |   ('Test', 'freq') |

|:-------------------|-------------------:|--------------------:|------------------:|-------------------:|

| No Finding         |              50500 |          0.483392   |              9861 |         0.266032   |

| Infiltration       |              13782 |          0.131923   |              6112 |         0.164891   |

| Effusion           |               8659 |          0.082885   |              4658 |         0.125664   |

| Atelectasis        |               8280 |          0.0792572  |              3279 |         0.0884614  |

| Nodule             |               4708 |          0.0450656  |              1623 |         0.0437856  |

| Mass               |               4034 |          0.038614   |              1748 |         0.0471578  |

| Consolidation      |               2852 |          0.0272997  |              1815 |         0.0489654  |

| Pneumothorax       |               2637 |          0.0252417  |              2665 |         0.0718968  |

| Pleural_Thickening |               2242 |          0.0214607  |              1143 |         0.0308361  |

| Cardiomegaly       |               1707 |          0.0163396  |              1069 |         0.0288397  |

| Emphysema          |               1423 |          0.0136211  |              1093 |         0.0294871  |

| Edema              |               1378 |          0.0131904  |               925 |         0.0249548  |

| Fibrosis           |               1251 |          0.0119747  |               435 |         0.0117355  |

| Pneumonia          |                876 |          0.00838518 |               555 |         0.0149729  |

| Hernia             |                141 |          0.00134967 |                86 |         0.00232012 |

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### License and attribution 

There are no restrictions on the use of the NIH chest x-ray images. However, the dataset has the following attribution requirements:

- Provide a link to the NIH download site: https://nihcc.app.box.com/v/ChestXray-NIHCC

- Include a citation to the CVPR 2017 paper (see Citation information section)

- Acknowledge that the NIH Clinical Center is the data provider

### Citation Information

```

@inproceedings{Wang_2017,

    doi = {10.1109/cvpr.2017.369},

    url = {https://doi.org/10.1109%2Fcvpr.2017.369},

    year = 2017,

    month = {jul},

    publisher = {{IEEE}

},

    author = {Xiaosong Wang and Yifan Peng and Le Lu and Zhiyong Lu and Mohammadhadi Bagheri and Ronald M. Summers},

    title = {{ChestX}-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases},

    booktitle = {2017 {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})}

}

```

### Contributions

Thanks to [@alcazar90](https://github.com/alcazar90) for adding this dataset.