alkzar90/NIH-Chest-X-ray-dataset

Dataset Card for NIH Chest X-ray dataset

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

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.
  Class Label Mappings

  ```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 } ```

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 for adding this dataset.