UniToPatho
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 20x 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.
For this benchmark we used only the 800 subset which contains 8669 images of resolution 1812x1812 (the 7000 subset contains much bigger images and would therefore be difficult to handle as patch classification task).
Raw data
Key stats
| Modality | Vision (WSI patches) |
| Task | Multiclass classification (6 classes) |
| Cancer type | Colorectal |
| Data size | 48.37 GB |
| Image dimension | 1812 x 1812 |
| Magnification (μm/px) | 20x (0.4415) |
| Magnification after resize (μm/px) | 162x (3.57) |
| Files format | png |
| Number of images | 8669 |
Splits
The data source provides train/validation splits
| Splits | Train | Validation |
|---|---|---|
| #Samples | 6270 (72.33) | 2399 (27.67%) |
The dataset authors only provide two splits, which is why we don't report performance on a third test split.
Organization
The UniToPatho data is organized as follows (note that we are using only the 800 subset):
unitopatho
├── 800
test.csv
train.csv
│ ├── HP # 1 folder per class
│ ├── NORM
│ ├── TA.HG
│ ├── ...
Download and preprocessing
The UniToPatho dataset class doesn't download the data during runtime and must be downloaded manually from the official source.