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

1University of Turin, Computer Science dept.
2University of Turin, Medical Sciences dept.

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.

You can download UniToPatho from IEEE-DataPort

We provide a PyTorch compatible dataset class and ECVL compatible dataloader. For example usage see Example.ipynb

If you use this dataset, please make sure to cite the related work:

@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}
}