metadata

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

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

@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}
}