README.md

---
license: mit
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
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: float32
  splits:
  - name: train
    num_bytes: 7855046412.21
    num_examples: 85283
  download_size: 7915527673
  dataset_size: 7855046412.21
---

# Dataset Card for Histopathology Dataset

## Dataset Summary
This dataset contains 224x224, 512x512 and 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.

![Data Processing](data_processing.png)

## Thumbnail of Main Slide

![Main Slide Thumbnail](test_001.png)

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("Cilem/histopathology")
display(dataset['train'][0]["image"])
```


## Supported Tasks
Machine learning applications that can be performed using this dataset:
* Classification
* Segmentation
* Image generation

## Languages
* English

## Dataset Structure
### Data Fields
- `image`: Image of the patch.
- `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`
  - [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.