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--- a +++ b/README.md @@ -0,0 +1,232 @@ +--- +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)_ + + + +## 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. +