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---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: slide_name
dtype: string
- name: x
dtype: int64
- name: y
dtype: int64
- name: level
dtype: int64
- name: patch_size
sequence: int64
- name: resize
sequence: int64
- name: embedding_vector
sequence:
sequence: float64
splits:
- name: train
num_bytes: 4289155572.625
num_examples: 40115
download_size: 4289233317
dataset_size: 4289155572.625
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for Histopathology Dataset
## Dataset Summary
This dataset contains 1024x1024 patches of a group of histopathology images taken from the [CAMELYON16](http://gigadb.org/dataset/100439) dataset and embedding vectors extracted from these patches using the [Google Path Foundation](https://huggingface.co/google/path-foundation) model.
## Thumbnail of Main Slide
## Usage
- [CAMELYON16](http://gigadb.org/dataset/100439): List of images taken from CAMELYON16 dataset:
* `test_001.tif`
* `test_002.tif`
* `test_003.tif`
* `test_004.tif`
* `test_005.tif`
* `test_006.tif`
* `test_007.tif`
* `test_008.tif`
* `test_009.tif`
* `test_010.tif`
* `test_011.tif`
* `test_012.tif`
* `test_013.tif`
* `test_014.tif`
* `test_015.tif`
* `test_016.tif`
* `test_017.tif`
* `test_018.tif`
* `test_019.tif`
* `test_020.tif`
* Create a folder named ***wsi*** and download the images given in the list. Then:
```python
from datasets import load_dataset
import openslide as ops
dataset = load_dataset("Cilem/histopathology-1024")
slide_name = dataset['train'][0]['slide_name']
resize = dataset['train'][0]['resize']
path = os.path.join('wsi', slide_name)
slide = ops.OpenSlide(path)
patch = slide.read_region((x, y), 0, patch_size)
patch = patch.resize(resize)
display(patch)
```
## Supported Tasks
Machine learning applications that can be performed using this dataset:
* Classification
* Segmentation
* Image generation
## Languages
* English
## Dataset Structure
### Data Fields
- `image`: Patch image.
- `slide_name`: Main slide name of the patch.
- `x`: X coordinate of the patch.
- `y`: Y coordinate of the patch.
- `level`: Level of the main slide.
- `patch_size`: Size of the patch.
- `resize`: Image size used to obtain embedding vector with Path foundation model.
- `embedding_vector`: Embedding vector of the patch extracted using Path foundation model.
## Dataset Creation
### Source Data
- **Original Sources**
- [CAMELYON16](http://gigadb.org/dataset/100439): List of images taken from CAMELYON16 dataset:
* `test_001.tif`
* `test_002.tif`
* `test_003.tif`
* `test_004.tif`
* `test_005.tif`
* `test_006.tif`
* `test_007.tif`
* `test_008.tif`
* `test_009.tif`
* `test_010.tif`
* `test_011.tif`
* `test_012.tif`
* `test_013.tif`
* `test_014.tif`
* `test_015.tif`
* `test_016.tif`
* `test_017.tif`
* `test_018.tif`
* `test_019.tif`
* `test_020.tif`
- [Google Path Foundation](https://huggingface.co/google/path-foundation): Embedding vectors extracted from the patches using the Path Foundation model.
## Considerations for Using the Data
### Social Impact and Bias
Attention should be paid to the Path Foundation model licenses provided by Google. |