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PanNuke
Classification
2D Polygon
Medical
|...
License: 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

CC BY-NC-SA 4.0

Data Summary
Type
Image,
Amount
205.343K
Size
1.93GB
Provided by
Department of Computer Science, University of Warwick, CV4 7AL
The department offers an excellent environment for PhD research studies. We host a Centre for Doctoral Training and Research in Computer Science with multiple scholarships available every year.
| Amount 205.343K | Size 1.93GB
PanNuke
Classification 2D Polygon
Medical
License: 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

CC BY-NC-SA 4.0

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