metadata
license: cc-by-nc-nd-3.0
language:
- en
tags:
- 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
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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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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. 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 = {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},
}
@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