task_categories:image-classification size_categories:10K<n<100K license:openrail medical arxiv:2306.01322 region:us
Chest XRay dataset with chexpert labels.
---
task_categories:
- image-classification
tags:
- medical
pretty_name: C
size_categories:
- 10K<n<100K
license: openrail
---
# Dataset Card for Synthetic Chest Xray

## Dataset Description

This is a synthetic chest X-ray dataset created during the development of the *privacy distillation* paper. In particular, this is the $D_{filter}$ dataset described.

- **Paper: https://arxiv.org/abs/2306.01322
- **Point of Contact: pedro.sanchez@ed.ac.uk

### Dataset Summary

This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).

### Supported Tasks

Chexpert classification.

https://stanfordmlgroup.github.io/competitions/chexpert/

## Dataset Structure

- Images
- Chexpert Labels

### Data Splits

We did not define data splits. In the paper, all the images were used as training data and real data samples were used as validation and testing data.

## Dataset Creation

We generated the synthetic data samples using the diffusion model finetuned on the [Mimic-CXR dataset](https://physionet.org/content/mimic-cxr/2.0.0/).


### Personal and Sensitive Information

Following GDPR "Personal data is any information that relates to an identified or identifiable living individual."

We make sure that there are not "personal data" (re-identifiable information) by filtering with a deep learning model trained for identifying patients.

## Considerations for Using the Data

### Social Impact of Dataset

We hope that this dataset can used to enhance AI models training for pathology classification in chest X-ray.

### Discussion of Biases

There are biases towards specific pathologies. For example, the "No Findings" label is much bigger than other less common pathologies.

## Additional Information

### Dataset Curators

We used deep learning to filter the dataset.

We filter for re-identification, making sure that none of the images used in the training can be re-identified using samples from this synthetic dataset.

### Licensing Information

We generated the synthetic data samples based on generative model trained on the [Mimic-CXR dataset](https://physionet.org/content/mimic-cxr/2.0.0/). Mimic-CXR uses the [PhysioNet Credentialed Health](https://physionet.org/content/mimic-cxr/view-license/2.0.0/) data license.

The real data license explicitly requires that "The LICENSEE will not share access to PhysioNet restricted data with anyone else". Here, we ensure that none of the synthetic images can be used to re-identify real Mimic-CXR images. Therefore, we do not consider this synthetic dataset to be "PhysioNet restricted data".

This dataset is released under the [Open & Responsible AI license ("OpenRAIL")](https://huggingface.co/blog/open_rail)

### Citation Information

Fernandez, V., Sanchez, P., Pinaya, W. H. L., Jacenków, G., Tsaftaris, S. A., & Cardoso, J. (2023). Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models. arXiv preprint arXiv:2306.01322.

https://arxiv.org/abs/2306.01322

### Contributions

Pedro P. Sanchez, Walter Pinaya uploaded the dataset to Huggingface. All other co-authors of the papers contributed for creating the dataset.