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NIH-Chest-X-ray-dataset

Tasks:
Image Classification
Sub-tasks:
multi-class-image-classification
Languages:
English
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100K<n<1M
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metadata
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

Dataset Description

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.