README.md

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

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: NIH Chest X-ray Dataset of 10 Common Thorax Disease Categories
• Repository:
• Paper: ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
• 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

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 } ```

Data Splits

	train	validation	test
# of examples	75750	25250	23132

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.