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KidRare

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pathology
pediatric
rare_cancer
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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.

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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 1,232 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. For rare cancer subtyping task, label file can be found in PathPT.

  • License: CC-BY-NC-ND-3.0

Citation

If you use this dataset in your research, please cite the following papers:

@article{zhou2024keep,
  title={A Knowledge-enhanced Pathology Vision-language Foundation Model for Cancer Diagnosis},
  author={Xiao Zhou, Luoyi Sun, Dexuan He, Wenbin Guan, Ruifen Wang, Lifeng Wang, Xin Sun, Kun Sun, Ya Zhang, Yanfeng Wang, Weidi Xie},
  journal={arXiv preprint arXiv:2412.13126},
  year={2024}
}

@misc{he2025boostingpathologyfoundationmodels,
      title={Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping}, 
      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 Xin Sun and Yanfeng Wang and Kun Sun and Ya Zhang and Weidi Xie},
      year={2025},
      eprint={2508.15904},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2508.15904}, 
}

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