Datasets:

alkzar90
/

NIH-Chest-X-ray-dataset

	---
	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.