Data
Meta_Album_PNU_Mini

Meta_Album_PNU_Mini

active ARFF CC BY-NC 4.0 Visibility: public Uploaded 28-10-2022 by Meta Album
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
## Meta-Album PanNuke Dataset (Mini) * 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: 760 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) [[Extended]](https://www.openml.org/d/44330)

3 features

CATEGORY (target)string19 unique values
0 missing
FILE_NAMEstring760 unique values
0 missing
SUPER_CATEGORYnumeric0 unique values
760 missing

19 properties

760
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).
760
Number of missing values in the dataset.
760
Number of instances with at least one value missing.
1
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
100
Percentage of instances having missing values.
1
Average class difference between consecutive instances.
33.33
Percentage of missing values.
0
Number of attributes divided by the number of instances.
33.33
Percentage of numeric attributes.
5.26
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
40
Number of instances belonging to the most frequent class.
5.26
Percentage of instances belonging to the least frequent class.
40
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

1 tasks

1 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: CATEGORY
Define a new task