Data
Meta_Album_PNU_Extended

Meta_Album_PNU_Extended

active ARFF CC BY-NC 4.0 Visibility: public Uploaded 08-11-2022 by Meta Album
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## Meta-Album PanNuke Dataset (Extended) * The PanNuke dataset(https://jgamper.github.io/PanNukeDataset/) is a semi-automatically generated segmentation and classification task of nuclei. The dataset contains 7 753 images of 19 different tissue types. For the Meta-Album meta-dataset, even though this dataset was designed as a segmentation task, we were able to transform it into a tissue classification task since we had the tissue type for each sample in the dataset. We also resized the images to 128x128 pixels and applied stain normalization to avoid bias and remove some spurious features. ### Dataset Details ![](https://meta-album.github.io/assets/img/samples/PNU.png) Meta Album ID: MCR.PNU Meta Album URL: [https://meta-album.github.io/datasets/PNU.html](https://meta-album.github.io/datasets/PNU.html) Domain ID: MCR Domain Name: Microscopic Dataset ID: PNU Dataset Name: PanNuke Short Description: 19 Human Tissues Dataset \# Classes: 19 \# Images: 5530 Keywords: microscopic, human tissues Data Format: images Image size: 128x128 License (original data release): Attribution-NonCommercial-ShareAlike 4.0 International License URL(original data release): https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke https://creativecommons.org/licenses/by-nc-sa/4.0/ License (Meta-Album data release): Attribution-NonCommercial-ShareAlike 4.0 International License URL (Meta-Album data release): [https://creativecommons.org/licenses/by-nc-sa/4.0/](https://creativecommons.org/licenses/by-nc-sa/4.0/) Source: PanNuke: An Open Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification Source URL: https://jgamper.github.io/PanNukeDataset/ Original Author: Gamper, Jevgenij and Koohbanani, Navid Alemi and Benet, Ksenija and Khuram, Ali and Rajpoot, Nasir Original contact: j.gamper@warwick.ac.uk Meta Album author: Romain Mussard Created Date: 01 March 2022 Contact Name: Ihsan Ullah Contact Email: meta-album@chalearn.org Contact URL: [https://meta-album.github.io/](https://meta-album.github.io/) ### Cite this dataset ``` @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} } ``` ### Cite Meta-Album ``` @inproceedings{meta-album-2022, title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification}, author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh}, booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, url = {https://meta-album.github.io/}, year = {2022} } ``` ### More For more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) For details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) Supporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) Meta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) ### Other versions of this dataset [[Micro]](https://www.openml.org/d/44312) [[Mini]](https://www.openml.org/d/44297)

3 features

CATEGORY (target)string19 unique values
0 missing
FILE_NAMEstring5530 unique values
0 missing
SUPER_CATEGORYnumeric0 unique values
5530 missing

19 properties

5530
Number of instances (rows) of the dataset.
3
Number of attributes (columns) of the dataset.
19
Number of distinct values of the target attribute (if it is nominal).
5530
Number of missing values in the dataset.
5530
Number of instances with at least one value missing.
1
Number of numeric attributes.
0
Number of nominal attributes.
101
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
100
Percentage of instances having missing values.
33.33
Percentage of missing values.
1
Average class difference between consecutive instances.
33.33
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
0
Percentage of nominal attributes.
17.49
Percentage of instances belonging to the most frequent class.
967
Number of instances belonging to the most frequent class.
1.83
Percentage of instances belonging to the least frequent class.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: CATEGORY
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