README.md

---
license: cc-by-nc-nd-4.0
size_categories:
- n<1K
task_categories:
- image-classification
tags:
- biology
- Histopathology
- Histology
- Digital Pathology
- Breast Cancer
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Benign
          '1': InSitu
          '2': Invasive
          '3': Normal
          '4': Unknown
  splits:
  - name: train
    num_bytes: 7370596186
    num_examples: 400
  - name: test
    num_bytes: 1887476013
    num_examples: 100
  download_size: 7727410763
  dataset_size: 9258072199
paperswithcode_id: bach
pretty_name: BreAst Cancer Histology
---

# BreAst Cancer Histology (BACH) Dataset: Grand Challenge on Breast Cancer Histology images

![](https://rumc-gcorg-p-public.s3.amazonaws.com/b/176/header_small.x10.jpeg)

## Dataset Description

- **Homepage**: https://iciar2018-challenge.grand-challenge.org
- **DOI**: https://doi.org/10.5281/zenodo.3632035
- **Publication Date** 2019-05-31

## Description

The dataset is composed of Hematoxylin and eosin (H&E) stained breast histology microscopy images.

Microscopy images are labelled as normal, benign, in situ carcinoma or invasive carcinoma according to the predominant cancer type in each image.
The annotation was performed by two medical experts and images where there was disagreement were discarded.
Images have the following specifications:
  * Color model: R(ed)G(reen)B(lue)
  * Size: 2048 x 1536 pixels
  * Pixel scale: 0.42 µm x 0.42 µm
  * Memory space: 10-20 MB (approx.)
  * Type of label: image-wise

## Citation

```bibtex
@dataset{polonia_2020_3632035,
  author    = {Polónia, António and Eloy, Catarina and Aguiar, Paulo},
  title     = {{BACH Dataset : Grand Challenge on Breast Cancer Histology images}},
  month     = jan,
  year      = 2020,
  publisher = {Zenodo}
}
```