task_categories:image-classification task_ids:multi-class-image-classification annotations_creators:machine-generated annotations_creators:expert-generated language_creators:machine-generated language_creators:expert-generated multilinguality:monolingual size_categories:100K<n<1M language:en license:unknown arxiv:1705.02315 region:us
The NIH Chest X-ray dataset consists of 100,000 de-identified images of chest x-rays. The images are in PNG format.

The data is provided by the NIH Clinical Center and is available through the NIH download site: https://nihcc.app.box.com/v/ChestXray-NIHCC
@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})}
}
---
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.

