patch_camelyon

• Description:

The PatchCamelyon benchmark is a new and challenging image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annoted with a binary label indicating presence of metastatic tissue. PCam provides a new benchmark for machine learning models: bigger than CIFAR10, smaller than Imagenet, trainable on a single GPU.

• Additional Documentation: Explore on Papers With Code

• Homepage: https://patchcamelyon.grand-challenge.org/

• Source code: tfds.datasets.patch_camelyon.Builder

• Versions:

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

• Download size: 7.48 GiB

• Dataset size: 7.06 GiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'test'	32,768
'train'	262,144
'validation'	32,768

• Feature structure:

FeaturesDict({
    'id': Text(shape=(), dtype=string),
    'image': Image(shape=(96, 96, 3), dtype=uint8),
    'label': ClassLabel(shape=(), dtype=int64, num_classes=2),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
id	Text		string
image	Image	(96, 96, 3)	uint8
label	ClassLabel		int64

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

• Figure (tfds.show_examples):

• Examples (tfds.as_dataframe):

• Citation:

@misc{b_s_veeling_j_linmans_j_winkens_t_cohen_2018_2546921,
  author       = {B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, M. Welling},
  title        = {Rotation Equivariant CNNs for Digital Pathology},
  month        = sep,
  year         = 2018,
  doi          = {10.1007/978-3-030-00934-2_24},
  url          = {https://doi.org/10.1007/978-3-030-00934-2_24}
}