---
license: cc-by-nc-nd-3.0
language:
- en
tags:
- pathology
- pediatric
- rare_cancer
extra_gated_prompt: >-
  This dataset and the associated code are released under the
  CC-BY-NC-ND-3.0 license and are intended for non-commercial,
  academic research use only, with proper attribution.
  Any form of commercial use, sale, or monetization of the dataset
  or its derivatives is strictly prohibited. Redistribution or
  public release of the dataset, in whole or in part, is not allowed.
  By downloading or accessing this dataset, you agree to comply with
  these terms. Commercial entities interested in using the dataset
  should contact the authors to request permission.
  
extra_gated_fields:
  Full name: text
  Affiliation: text
  Type of affiliation:
    type: select
    options:
    - Academia
    - Industry
    - label: Other
      value: other
  Official email (must match primary email in your Hugging Face account; work email instead of personal): text
  Please explain your intended research use: text
  I agree to all terms outlined above: checkbox
  I agree to use this dataset for non-commercial, academic purposes only: checkbox
  I agree not to distribute the dataset, if another user within your organization wishes to use the dataset, they must register as an individual user: checkbox
pipeline_tag: image-classification
---

# KidRare: A WSI Dataset for Rare Pediatric Pathology

## Dataset Description

KidRare is a specialized Whole Slide Image (WSI) dataset focused on rare pediatric tumors. 
It contains a total of **2,331 Whole Slide Images (WSIs)** covering four distinct types of pediatric cancers: **Neuroblastoma**, **Nephroblastoma**, **Medulloblastoma**, **Hepatoblastoma**. 
It is designed to facilitate research in computational pathology, specifically for tasks such as cancer diagnosis and subtype classification.

For cancer diagnosis and subtyping tasks, label file can be found in [KEEP](https://github.com/MAGIC-AI4Med/KEEP "KEEP Github Repo").
For rare cancer subtyping task, label file can be found in [PathPT](https://github.com/MAGIC-AI4Med/PathPT/blob/main/multifold/dataset_division.json "PathPT Github Repo").

- **License:** CC-BY-NC-ND-3.0

## Citation

If you use this dataset in your research, please cite the following papers:
```bibtex
@article{zhou2024keep,
title = {Knowledge-enhanced pretraining for vision-language pathology foundation model on cancer diagnosis},
journal = {Cancer Cell},
year = {2026},
issn = {1535-6108},
doi = {https://doi.org/10.1016/j.ccell.2026.01.019},
url = {https://www.sciencedirect.com/science/article/pii/S1535610826000589},
author = {Xiao Zhou and Luoyi Sun and Dexuan He and Wenbin Guan and Ge Wang and Ruifen Wang and Lifeng Wang and Xiaojun Yuan and Xin Sun and Ya Zhang and Kun Sun and Yanfeng Wang and Weidi Xie},
keywords = {pathology foundation model, cancer diagnosis, vision-language, knowledge enhancement},
}
```

```bibtex
@article{he2026boosting,
  author = {Dexuan He and Xiao Zhou and Wenbin Guan and Liyuan Zhang and Xiaoman Zhang and Sinuo Xu and Ge Wang and Lifeng Wang and Xiaojun Yuan and Jing Ma and Xin Sun and Yanfeng Wang and Kun Sun and Ya Zhang and Weidi Xie},
  title = {Boosting pathology foundation models via few-shot prompt-tuning for rare cancer subtyping},
  journal = {Nature Communications},
  year = {2026},
  doi = {10.1038/s41467-026-71715-2},
  url = {https://doi.org/10.1038/s41467-026-71715-2}
}
```

## Contact
For questions regarding the dataset, please contact:
- Email: firehdx233@sjtu.edu.cn