Create a dataset card

README.md

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+---
+annotations_creators:
+- machine-generated
+- expert-generated
+language_creators:
+- machine-generated
+- expert-generated
+language:
+- en
+license:
+- unknown
+multilinguality:
+- monolingual
+pretty_name: NIH-CXR8
+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>
+
+### 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](https://github.com/alcazar90) for adding this dataset.
+