patch_camelyon

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.

Features

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

Statistics

Split Examples
ALL 327,680
TRAIN 262,144
TEST 32,768
VALIDATION 32,768

Homepage

Supervised keys (for as_supervised=True)

(u'image', u'label')

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}
}