task_categories:image-classification size_categories:10K<n<100K license:openrail medical arxiv:2112.01330 arxiv:2307.15208 region:us
---
task_categories:
- image-classification
tags:
- medical
pretty_name: C
size_categories:
- 10K<n<100K
license: openrail
---
# Dataset Card for Synthetic CSAW 100k Mammograms

## Dataset Description

This is a synthetic mammogram dataset created with the latent diffusion model from *Generative AI for Medical Imaging: extending the MONAI Framework* paper.
The generative model was trained on the [CSAW-M dataset](https://arxiv.org/abs/2112.01330).

- **Paper: https://arxiv.org/abs/2307.15208
- **Point of Contact: walter.diaz_sanz@kcl.ac.uk

### Dataset Summary

### Supported Tasks

Classification masking of cancer in mammogram. 
The dataset contains 100k synthetic mammograms with 3 labels: 
- "Low masking level" (score <= 2), 
- "Medium masking level" (2 < score <= 6),
- "High masking level" (score > 6).


## Dataset Structure

- Images
- CSAW-M Labels

### Data Splits

We did not define data splits.

## Dataset Creation

We generated the synthetic data samples using the diffusion model finetuned on the [CSAW-M dataset](https://arxiv.org/abs/2112.01330).

### 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 cancer masking.

### Discussion of Biases

There are biases towards specific pathologies.

## Additional Information

### Dataset Curators

### Licensing Information

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

### Citation Information

Pinaya, W. H., Graham, M. S., Kerfoot, E., Tudosiu, P. D., Dafflon, J., Fernandez, V., ... & Cardoso, M. J. (2023). Generative ai for medical imaging: extending the monai framework. arXiv preprint arXiv:2307.15208.

https://arxiv.org/abs/2307.15208
