NIHChestXRay / Git / Diff of /README.md

Datasets:

GeorgeSullivan/

NIHChestXRay

Downloads: 1

Data:

De-identified

Image Specialty:

Radiology

Respiratory Medicine Medical Imaging Technique:

X-Ray Medical Imaging Region:

Chest

Lungs Clinical Purpose:

Diagnosis Task:

Classification License:

Unknown

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# 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.


	a/README.md		b/README.md
1	---	1	# Dataset Card for NIH Chest X-ray dataset
2	annotations_creators:	2
3	- machine-generated
4	- expert-generated
5	language_creators:
6	- machine-generated
7	- expert-generated
8	language:
9	- en
10	license:
11	- unknown
12	multilinguality:
13	- monolingual
14	pretty_name: NIH-CXR14
15	paperswithcode_id: chestx-ray14
16	size_categories:
17	- 100K<n<1M
18	task_categories:
19	- image-classification
20	task_ids:
21	- multi-class-image-classification
22	---
23
24	# Dataset Card for NIH Chest X-ray dataset
25
26	## Table of Contents	3	## Table of Contents
27		4
28	- [Table of Contents](#table-of-contents)	5	- [Table of Contents](#table-of-contents)
29	- [Dataset Description](#dataset-description)	6	- [Dataset Description](#dataset-description)
30	- [Dataset Summary](#dataset-summary)	7	- [Dataset Summary](#dataset-summary)
31	- [Languages](#languages)	8	- [Languages](#languages)
32	- [Dataset Structure](#dataset-structure)	9	- [Dataset Structure](#dataset-structure)
33	- [Data Instances](#data-instances)	10	- [Data Instances](#data-instances)
34	- [Data Fields](#data-fields)	11	- [Data Fields](#data-fields)
35	- [Data Splits](#data-splits)	12	- [Data Splits](#data-splits)
36	- [Dataset Creation](#dataset-creation)	13	- [Dataset Creation](#dataset-creation)
37	- [Curation Rationale](#curation-rationale)	14	- [Curation Rationale](#curation-rationale)
38	- [Source Data](#source-data)	15	- [Source Data](#source-data)
39	- [Annotations](#annotations)	16	- [Annotations](#annotations)
40	- [Personal and Sensitive Information](#personal-and-sensitive-information)	17	- [Personal and Sensitive Information](#personal-and-sensitive-information)
41	- [Considerations for Using the Data](#considerations-for-using-the-data)	18	- [Considerations for Using the Data](#considerations-for-using-the-data)
42	- [Social Impact of Dataset](#social-impact-of-dataset)	19	- [Social Impact of Dataset](#social-impact-of-dataset)
43	- [Discussion of Biases](#discussion-of-biases)	20	- [Discussion of Biases](#discussion-of-biases)
44	- [Other Known Limitations](#other-known-limitations)	21	- [Other Known Limitations](#other-known-limitations)
45	- [Additional Information](#additional-information)	22	- [Additional Information](#additional-information)
46	- [Dataset Curators](#dataset-curators)	23	- [Dataset Curators](#dataset-curators)
47	- [Licensing Information](#licensing-information)	24	- [Licensing Information](#licensing-information)
48	- [Citation Information](#citation-information)	25	- [Citation Information](#citation-information)
49	- [Contributions](#contributions)	26	- [Contributions](#contributions)
50		27
51	## Dataset Description	28	## Dataset Description
52		29
53	- Homepage: [NIH Chest X-ray Dataset of 10 Common Thorax Disease Categories](https://nihcc.app.box.com/v/ChestXray-NIHCC/folder/36938765345)	30	- Homepage: [NIH Chest X-ray Dataset of 10 Common Thorax Disease Categories](https://nihcc.app.box.com/v/ChestXray-NIHCC/folder/36938765345)
54	- Repository:	31	- Repository:
55	- 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)	32	- 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)
56	- Leaderboard:	33	- Leaderboard:
57	- Point of Contact: rms@nih.gov	34	- Point of Contact: rms@nih.gov
58		35
59	### Dataset Summary	36	### Dataset Summary
60		37
61	_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)_	38	_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)_
62		39
63	![](https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/data/nih-chest-xray14-portraint.png)	40	![](https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/data/nih-chest-xray14-portraint.png)
64		41
65	## Dataset Structure	42	## Dataset Structure
66		43
67	### Data Instances	44	### Data Instances
68		45
69	A sample from the training set is provided below:	46	A sample from the training set is provided below:
70		47
71	```	48	```
72	{'image_file_path': '/root/.cache/huggingface/datasets/downloads/extracted/95db46f21d556880cf0ecb11d45d5ba0b58fcb113c9a0fff2234eba8f74fe22a/images/00000798_022.png',	49	{'image_file_path': '/root/.cache/huggingface/datasets/downloads/extracted/95db46f21d556880cf0ecb11d45d5ba0b58fcb113c9a0fff2234eba8f74fe22a/images/00000798_022.png',
73	'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=1024x1024 at 0x7F2151B144D0>,	50	'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=1024x1024 at 0x7F2151B144D0>,
74	'labels': [9, 3]}	51	'labels': [9, 3]}
75	```	52	```
76		53
77	### Data Fields	54	### Data Fields
78		55
79	The data instances have the following fields:	56	The data instances have the following fields:
80	- `image_file_path` a `str` with the image path	57	- `image_file_path` a `str` with the image path
81	- `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]`.	58	- `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]`.
82	- `labels`: an `int` classification label.	59	- `labels`: an `int` classification label.
83	<details>	60	<details>
84	<summary>Class Label Mappings</summary>	61	<summary>Class Label Mappings</summary>
85	```json	62	```json
86	{	63	{
87	"No Finding": 0,	64	"No Finding": 0,
88	"Atelectasis": 1,	65	"Atelectasis": 1,
89	"Cardiomegaly": 2,	66	"Cardiomegaly": 2,
90	"Effusion": 3,	67	"Effusion": 3,
91	"Infiltration": 4,	68	"Infiltration": 4,
92	"Mass": 5,	69	"Mass": 5,
93	"Nodule": 6,	70	"Nodule": 6,
94	"Pneumonia": 7,	71	"Pneumonia": 7,
95	"Pneumothorax": 8,	72	"Pneumothorax": 8,
96	"Consolidation": 9,	73	"Consolidation": 9,
97	"Edema": 10,	74	"Edema": 10,
98	"Emphysema": 11,	75	"Emphysema": 11,
99	"Fibrosis": 12,	76	"Fibrosis": 12,
100	"Pleural_Thickening": 13,	77	"Pleural_Thickening": 13,
101	"Hernia": 14	78	"Hernia": 14
102	}	79	}
103	```	80	```
104	</details>	81	</details>
105		82
106	Label distribution on the dataset:	83	Label distribution on the dataset:
107		84
108	\| labels \| obs \| freq \|	85	\| labels \| obs \| freq \|
109	\|:-------------------\|------:\|-----------:\|	86	\|:-------------------\|------:\|-----------:\|
110	\| No Finding \| 60361 \| 0.426468 \|	87	\| No Finding \| 60361 \| 0.426468 \|
111	\| Infiltration \| 19894 \| 0.140557 \|	88	\| Infiltration \| 19894 \| 0.140557 \|
112	\| Effusion \| 13317 \| 0.0940885 \|	89	\| Effusion \| 13317 \| 0.0940885 \|
113	\| Atelectasis \| 11559 \| 0.0816677 \|	90	\| Atelectasis \| 11559 \| 0.0816677 \|
114	\| Nodule \| 6331 \| 0.0447304 \|	91	\| Nodule \| 6331 \| 0.0447304 \|
115	\| Mass \| 5782 \| 0.0408515 \|	92	\| Mass \| 5782 \| 0.0408515 \|
116	\| Pneumothorax \| 5302 \| 0.0374602 \|	93	\| Pneumothorax \| 5302 \| 0.0374602 \|
117	\| Consolidation \| 4667 \| 0.0329737 \|	94	\| Consolidation \| 4667 \| 0.0329737 \|
118	\| Pleural_Thickening \| 3385 \| 0.023916 \|	95	\| Pleural_Thickening \| 3385 \| 0.023916 \|
119	\| Cardiomegaly \| 2776 \| 0.0196132 \|	96	\| Cardiomegaly \| 2776 \| 0.0196132 \|
120	\| Emphysema \| 2516 \| 0.0177763 \|	97	\| Emphysema \| 2516 \| 0.0177763 \|
121	\| Edema \| 2303 \| 0.0162714 \|	98	\| Edema \| 2303 \| 0.0162714 \|
122	\| Fibrosis \| 1686 \| 0.0119121 \|	99	\| Fibrosis \| 1686 \| 0.0119121 \|
123	\| Pneumonia \| 1431 \| 0.0101104 \|	100	\| Pneumonia \| 1431 \| 0.0101104 \|
124	\| Hernia \| 227 \| 0.00160382 \|	101	\| Hernia \| 227 \| 0.00160382 \|
125		102
126	### Data Splits	103	### Data Splits
127		104
128		105
129	\| \|train\| test\|	106	\| \|train\| test\|
130	\|-------------\|----:\|----:\|	107	\|-------------\|----:\|----:\|
131	\|# of examples\|86524\|25596\|	108	\|# of examples\|86524\|25596\|
132		109
133		110
134	Label distribution by dataset split:	111	Label distribution by dataset split:
135		112
136	\| labels \| ('Train', 'obs') \| ('Train', 'freq') \| ('Test', 'obs') \| ('Test', 'freq') \|	113	\| labels \| ('Train', 'obs') \| ('Train', 'freq') \| ('Test', 'obs') \| ('Test', 'freq') \|
137	\|:-------------------\|-------------------:\|--------------------:\|------------------:\|-------------------:\|	114	\|:-------------------\|-------------------:\|--------------------:\|------------------:\|-------------------:\|
138	\| No Finding \| 50500 \| 0.483392 \| 9861 \| 0.266032 \|	115	\| No Finding \| 50500 \| 0.483392 \| 9861 \| 0.266032 \|
139	\| Infiltration \| 13782 \| 0.131923 \| 6112 \| 0.164891 \|	116	\| Infiltration \| 13782 \| 0.131923 \| 6112 \| 0.164891 \|
140	\| Effusion \| 8659 \| 0.082885 \| 4658 \| 0.125664 \|	117	\| Effusion \| 8659 \| 0.082885 \| 4658 \| 0.125664 \|
141	\| Atelectasis \| 8280 \| 0.0792572 \| 3279 \| 0.0884614 \|	118	\| Atelectasis \| 8280 \| 0.0792572 \| 3279 \| 0.0884614 \|
142	\| Nodule \| 4708 \| 0.0450656 \| 1623 \| 0.0437856 \|	119	\| Nodule \| 4708 \| 0.0450656 \| 1623 \| 0.0437856 \|
143	\| Mass \| 4034 \| 0.038614 \| 1748 \| 0.0471578 \|	120	\| Mass \| 4034 \| 0.038614 \| 1748 \| 0.0471578 \|
144	\| Consolidation \| 2852 \| 0.0272997 \| 1815 \| 0.0489654 \|	121	\| Consolidation \| 2852 \| 0.0272997 \| 1815 \| 0.0489654 \|
145	\| Pneumothorax \| 2637 \| 0.0252417 \| 2665 \| 0.0718968 \|	122	\| Pneumothorax \| 2637 \| 0.0252417 \| 2665 \| 0.0718968 \|
146	\| Pleural_Thickening \| 2242 \| 0.0214607 \| 1143 \| 0.0308361 \|	123	\| Pleural_Thickening \| 2242 \| 0.0214607 \| 1143 \| 0.0308361 \|
147	\| Cardiomegaly \| 1707 \| 0.0163396 \| 1069 \| 0.0288397 \|	124	\| Cardiomegaly \| 1707 \| 0.0163396 \| 1069 \| 0.0288397 \|
148	\| Emphysema \| 1423 \| 0.0136211 \| 1093 \| 0.0294871 \|	125	\| Emphysema \| 1423 \| 0.0136211 \| 1093 \| 0.0294871 \|
149	\| Edema \| 1378 \| 0.0131904 \| 925 \| 0.0249548 \|	126	\| Edema \| 1378 \| 0.0131904 \| 925 \| 0.0249548 \|
150	\| Fibrosis \| 1251 \| 0.0119747 \| 435 \| 0.0117355 \|	127	\| Fibrosis \| 1251 \| 0.0119747 \| 435 \| 0.0117355 \|
151	\| Pneumonia \| 876 \| 0.00838518 \| 555 \| 0.0149729 \|	128	\| Pneumonia \| 876 \| 0.00838518 \| 555 \| 0.0149729 \|
152	\| Hernia \| 141 \| 0.00134967 \| 86 \| 0.00232012 \|	129	\| Hernia \| 141 \| 0.00134967 \| 86 \| 0.00232012 \|
153		130
154	## Dataset Creation	131	## Dataset Creation
155		132
156	### Curation Rationale	133	### Curation Rationale
157		134
158	[More Information Needed]	135	[More Information Needed]
159		136
160	### Source Data	137	### Source Data
161		138
162	#### Initial Data Collection and Normalization	139	#### Initial Data Collection and Normalization
163		140
164	[More Information Needed]	141	[More Information Needed]
165		142
166	#### Who are the source language producers?	143	#### Who are the source language producers?
167		144
168	[More Information Needed]	145	[More Information Needed]
169		146
170	### Annotations	147	### Annotations
171		148
172	#### Annotation process	149	#### Annotation process
173		150
174	[More Information Needed]	151	[More Information Needed]
175		152
176	#### Who are the annotators?	153	#### Who are the annotators?
177		154
178	[More Information Needed]	155	[More Information Needed]
179		156
180	### Personal and Sensitive Information	157	### Personal and Sensitive Information
181		158
182	[More Information Needed]	159	[More Information Needed]
183		160
184	## Considerations for Using the Data	161	## Considerations for Using the Data
185		162
186	### Social Impact of Dataset	163	### Social Impact of Dataset
187		164
188	[More Information Needed]	165	[More Information Needed]
189		166
190	### Discussion of Biases	167	### Discussion of Biases
191		168
192	[More Information Needed]	169	[More Information Needed]
193		170
194	### Other Known Limitations	171	### Other Known Limitations
195		172
196	[More Information Needed]	173	[More Information Needed]
197		174
198	## Additional Information	175	## Additional Information
199		176
200	### Dataset Curators	177	### Dataset Curators
201		178
202	[More Information Needed]	179	[More Information Needed]
203		180
204	### License and attribution	181	### License and attribution
205		182
206	There are no restrictions on the use of the NIH chest x-ray images. However, the dataset has the following attribution requirements:	183	There are no restrictions on the use of the NIH chest x-ray images. However, the dataset has the following attribution requirements:
207		184
208	- Provide a link to the NIH download site: https://nihcc.app.box.com/v/ChestXray-NIHCC	185	- Provide a link to the NIH download site: https://nihcc.app.box.com/v/ChestXray-NIHCC
209	- Include a citation to the CVPR 2017 paper (see Citation information section)	186	- Include a citation to the CVPR 2017 paper (see Citation information section)
210	- Acknowledge that the NIH Clinical Center is the data provider	187	- Acknowledge that the NIH Clinical Center is the data provider
211		188
212		189
213	### Citation Information	190	### Citation Information
214		191
215	```	192	```
216	@inproceedings{Wang_2017,	193	@inproceedings{Wang_2017,
217	doi = {10.1109/cvpr.2017.369},	194	doi = {10.1109/cvpr.2017.369},
218	url = {https://doi.org/10.1109%2Fcvpr.2017.369},	195	url = {https://doi.org/10.1109%2Fcvpr.2017.369},
219	year = 2017,	196	year = 2017,
220	month = {jul},	197	month = {jul},
221	publisher = {{IEEE}	198	publisher = {{IEEE}
222	},	199	},
223	author = {Xiaosong Wang and Yifan Peng and Le Lu and Zhiyong Lu and Mohammadhadi Bagheri and Ronald M. Summers},	200	author = {Xiaosong Wang and Yifan Peng and Le Lu and Zhiyong Lu and Mohammadhadi Bagheri and Ronald M. Summers},
224	title = {{ChestX}-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases},	201	title = {{ChestX}-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases},
225	booktitle = {2017 {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})}	202	booktitle = {2017 {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})}
226	}	203	}
227	```	204	```
228		205
229	### Contributions	206	### Contributions
230		207
231	Thanks to [@alcazar90](https://github.com/alcazar90) for adding this dataset.	208	Thanks to [@alcazar90](https://github.com/alcazar90) for adding this dataset.
232		209