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PanNuke
2D Classification
2D Polygon
Medical
|...
许可协议: CC-BY-NC-SA 4.0

Overview

Semi automatically generated nuclei instance segmentation and classification dataset with exhaustive nuclei labels across 19 different tissue types. The dataset consists of 481 visual fields, of which 312 are randomly sampled from more than 20K whole slide images at different magnifications, from multiple data sources. In total the dataset contains 205,343 labeled nuclei, each with an instance segmentation mask. Models trained on pannuke can aid in whole slide image tissue type segmentation, and generalise to new tissues. PanNuke demonstrates one of the first succesfully semi-automatically generated datasets.

Data Format

img

Samples from exhaustively annotated PanNuke dataset, that contains image patches from 19 tissue types for nuclei instance segmentation and classification (Red: Neoplastic; Green: Inflammatory; Dark Blue: Connective; Yellow: Dead; Orange: Epithelial)


Nuclei Type Statistics

imgA comparative plot of class distributions per tissue. Numbers in parenthesis represent the total number of nuclei within that category or tissue type.

Citation

@inproceedings{gamper2019pannuke,
  title={PanNuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification},
  author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Benet, Ksenija and Khuram, Ali and Rajpoot, Nasir},
  booktitle={European Congress on Digital Pathology},
  pages={11--19},
  year={2019},
  organization={Springer}
}

@article{gamper2020pannuke,
  title={PanNuke Dataset Extension, Insights and Baselines},
  author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Graham, Simon and Jahanifar, Mostafa and Khurram, Syed Ali and Azam, Ayesha and Hewitt, Katherine and Rajpoot, Nasir},
  journal={arXiv preprint arXiv:2003.10778},
  year={2020}
}

License

  1. The dataset provided here is for research purposes only. Commercial uses are not allowed. The data is licensed under the following license

    Attribution-NonCommercial-ShareAlike 4.0 International

    Creative Commons License

  2. If you intend to publish research work that uses this dataset, you must cite our papers (as mentioned above), wherein the same dataset was first used.

数据概要
数据格式
image,
数据量
205.343K
文件大小
1.93GB
发布方
Department of Computer Science, University of Warwick, CV4 7AL
The degree courses provided by the Department of Computer Science are internationally renowned for their blend of foundational rigour and research-led teaching in cutting-edge domains. Students graduate with the knowledge, skills and confidence to forge careers at the forefront of science and industry.
| 数据量 205.343K | 大小 1.93GB
PanNuke
2D Classification 2D Polygon
Medical
许可协议: CC-BY-NC-SA 4.0

Overview

Semi automatically generated nuclei instance segmentation and classification dataset with exhaustive nuclei labels across 19 different tissue types. The dataset consists of 481 visual fields, of which 312 are randomly sampled from more than 20K whole slide images at different magnifications, from multiple data sources. In total the dataset contains 205,343 labeled nuclei, each with an instance segmentation mask. Models trained on pannuke can aid in whole slide image tissue type segmentation, and generalise to new tissues. PanNuke demonstrates one of the first succesfully semi-automatically generated datasets.

Data Format

img

Samples from exhaustively annotated PanNuke dataset, that contains image patches from 19 tissue types for nuclei instance segmentation and classification (Red: Neoplastic; Green: Inflammatory; Dark Blue: Connective; Yellow: Dead; Orange: Epithelial)


Nuclei Type Statistics

imgA comparative plot of class distributions per tissue. Numbers in parenthesis represent the total number of nuclei within that category or tissue type.

Citation

@inproceedings{gamper2019pannuke,
  title={PanNuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification},
  author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Benet, Ksenija and Khuram, Ali and Rajpoot, Nasir},
  booktitle={European Congress on Digital Pathology},
  pages={11--19},
  year={2019},
  organization={Springer}
}

@article{gamper2020pannuke,
  title={PanNuke Dataset Extension, Insights and Baselines},
  author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Graham, Simon and Jahanifar, Mostafa and Khurram, Syed Ali and Azam, Ayesha and Hewitt, Katherine and Rajpoot, Nasir},
  journal={arXiv preprint arXiv:2003.10778},
  year={2020}
}

License

  1. The dataset provided here is for research purposes only. Commercial uses are not allowed. The data is licensed under the following license

    Attribution-NonCommercial-ShareAlike 4.0 International

    Creative Commons License

  2. If you intend to publish research work that uses this dataset, you must cite our papers (as mentioned above), wherein the same dataset was first used.

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