Datasets:
Tasks:
Image Classification
Sub-tasks:
multi-class-image-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
ArXiv:
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File size: 8,795 Bytes
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---
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)_
## 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.
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