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--- |
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license: cc-by-nc-nd-3.0 |
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language: |
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- en |
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tags: |
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- pathology |
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- pediatric |
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- rare_cancer |
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extra_gated_prompt: >- |
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This dataset and the associated code are released under the |
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CC-BY-NC-ND-3.0 license and are intended for non-commercial, |
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academic research use only, with proper attribution. |
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Any form of commercial use, sale, or monetization of the dataset |
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or its derivatives is strictly prohibited. Redistribution or |
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public release of the dataset, in whole or in part, is not allowed. |
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By downloading or accessing this dataset, you agree to comply with |
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these terms. Commercial entities interested in using the dataset |
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should contact the authors to request permission. |
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extra_gated_fields: |
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Full name: text |
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Affiliation: text |
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Type of affiliation: |
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type: select |
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options: |
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- Academia |
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- Industry |
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- label: Other |
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value: other |
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Official email (must match primary email in your Hugging Face account; work email instead of personal): text |
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Please explain your intended research use: text |
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I agree to all terms outlined above: checkbox |
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I agree to use this dataset for non-commercial, academic purposes only: checkbox |
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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 |
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pipeline_tag: image-classification |
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--- |
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# KidRare: A WSI Dataset for Rare Pediatric Pathology |
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## Dataset Description |
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KidRare is a specialized Whole Slide Image (WSI) dataset focused on rare pediatric tumors. |
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It contains a total of **926 Whole Slide Images (WSIs)** covering four distinct types of pediatric cancers: **Neuroblastoma**, **Nephroblastoma**, **Medulloblastoma**, **Hepatoblastoma**. |
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Diagnostic labels for each slide are provided in the corresponding **JSON** files. |
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It is designed to facilitate research in computational pathology, specifically for tasks such as cancer diagnosis and subtype classification. |
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- **License:** CC-BY-NC-ND-3.0 |
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## Citation |
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If you use this dataset in your research, please cite the following paper: |
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```bibtex |
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@article{zhou2024keep, |
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title={A Knowledge-enhanced Pathology Vision-language Foundation Model for Cancer Diagnosis}, |
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author={Xiao Zhou, Luoyi Sun, Dexuan He, Wenbin Guan, Ruifen Wang, Lifeng Wang, Xin Sun, Kun Sun, Ya Zhang, Yanfeng Wang, Weidi Xie}, |
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journal={arXiv preprint arXiv:2412.13126}, |
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year={2024} |
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} |
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``` |
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## Contact |
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For questions regarding the dataset, please contact: |
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- Email: firehdx233@sjtu.edu.cn |
