colorectal_histology

• Visualization: Explore in Know Your Data north_east

• Description:

Classification of textures in colorectal cancer histology. Each example is a 150 x 150 x 3 RGB image of one of 8 classes.

• Homepage: https://zenodo.org/record/53169#.XGZemKwzbmG

• Source code: tfds.image_classification.ColorectalHistology

• Versions:

  • 2.0.0 (default): New split API (https://tensorflow.org/datasets/splits)

• Download size: 246.14 MiB

• Dataset size: Unknown size

• Auto-cached (documentation): Unknown

• Splits:

Split	Examples
'train'	5,000

• Feature structure:

FeaturesDict({
    'filename': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(150, 150, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=8),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
filename	Text		tf.string
image	Image	(150, 150, 3)	tf.uint8
label	ClassLabel		tf.int64

• Supervised keys (See as_supervised doc): ('image', 'label')

• Figure (tfds.show_examples):

• Examples (tfds.as_dataframe):

• Citation:

@article{kather2016multi,
  title={Multi-class texture analysis in colorectal cancer histology},
  author={Kather, Jakob Nikolas and Weis, Cleo-Aron and Bianconi, Francesco and Melchers, Susanne M and Schad, Lothar R and Gaiser, Timo and Marx, Alexander and Z{"o}llner, Frank Gerrit},
  journal={Scientific reports},
  volume={6},
  pages={27988},
  year={2016},
  publisher={Nature Publishing Group}
}