https://github.com/EIDOSLAB/UNITOPATHO

Dataset of 9536 H&E-stained patches for colorectal polyps classification and adenomas grading | ICIP21 https://doi.org/10.1109/ICIP42928.2021.9506198
https://github.com/EIDOSLAB/UNITOPATHO

cancer data health histopathological-image histopathology histopathology-images medical-image-processing medical-images neural-networks

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Dataset of 9536 H&E-stained patches for colorectal polyps classification and adenomas grading | ICIP21 https://doi.org/10.1109/ICIP42928.2021.9506198

• Host: GitHub
• URL: https://github.com/EIDOSLAB/UNITOPATHO
• Owner: EIDOSLAB
• License: mit
• Created: 2021-02-09T10:09:02.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2023-05-18T14:53:57.000Z (about 2 years ago)
• Last Synced: 2024-12-08T05:03:09.097Z (5 months ago)
• Topics: cancer, data, health, histopathological-image, histopathology, histopathology-images, medical-image-processing, medical-images, neural-networks
• Language: Jupyter Notebook
• Homepage: https://ieee-dataport.org/open-access/unitopatho
• Size: 3.18 MB
• Stars: 31
• Watchers: 3
• Forks: 5
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-pathology - UNITOPATHO - A labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading. (Data / Datasets)

README

        
# UNITOPATHO 

## A Labeled Histopathological Dataset for Colorectal Polyps Classification and Adenoma Dysplasia Grading

Carlo Alberto Barbano1, Daniele Perlo1, Enzo Tartaglione1, Attilio Fiandrotti1, Luca Bertero2, Paola Cassoni2, Marco Grangetto1 

| [[pdf](https://ieeexplore.ieee.org/document/9506198)]

1University of Turin, Computer Science dept.


2University of Turin, Medical Sciences dept.




![UniToPatho](assets/unitopatho.png)

*UniToPatho* is an annotated dataset of **9536** hematoxylin and eosin stained patches extracted from 292 whole-slide images, meant for training deep neural networks for colorectal polyps classification and adenomas grading. The slides are acquired through a Hamamatsu Nanozoomer S210 scanner at 20× magnification (0.4415 μm/px). Each slide belongs to a different patient and is annotated by expert pathologists, according to six classes as follows:

- **NORM** - Normal tissue;

- **HP** - Hyperplastic Polyp;

- **TA.HG** - Tubular Adenoma, High-Grade dysplasia;

- **TA.LG** - Tubular Adenoma, Low-Grade dysplasia;

- **TVA.HG** - Tubulo-Villous Adenoma, High-Grade dysplasia;

- **TVA.LG** - Tubulo-Villous Adenoma, Low-Grade dysplasia.

## Downloading the dataset

You can download UniToPatho from [IEEE-DataPort](https://ieee-dataport.org/open-access/unitopatho)

## Dataloader and example usage

We provide a [PyTorch compatible dataset class](/unitopatho.py) and [ECVL compatible dataloader](/unitopatho_ecvl.py).

For example usage see [Example.ipynb](/Example.ipynb)

## Citation

If you use this dataset, please make sure to cite the [related work](https://arxiv.org/abs/2101.09991):

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/unitopatho-a-labeled-histopathological/colorectal-polyps-characterization-on)](https://paperswithcode.com/sota/colorectal-polyps-characterization-on?p=unitopatho-a-labeled-histopathological)

```

@INPROCEEDINGS{barbano2021unitopatho,

  author={Barbano, Carlo Alberto and Perlo, Daniele and Tartaglione, Enzo and Fiandrotti, Attilio and Bertero, Luca and Cassoni, Paola and Grangetto, Marco},

  booktitle={2021 IEEE International Conference on Image Processing (ICIP)}, 

  title={Unitopatho, A Labeled Histopathological Dataset for Colorectal Polyps Classification and Adenoma Dysplasia Grading}, 

  year={2021},

  volume={},

  number={},

  pages={76-80},

  doi={10.1109/ICIP42928.2021.9506198}

}

```