Dataset Open Access

Rotation Equivariant CNNs for Digital Pathology

B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, M. Welling


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  <dc:creator>B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, M. Welling</dc:creator>
  <dc:date>2018-09-26</dc:date>
  <dc: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.</dc:description>
  <dc:identifier>https://zenodo.org/record/2546921</dc:identifier>
  <dc:identifier>10.1007/978-3-030-00934-2_24</dc:identifier>
  <dc:identifier>oai:zenodo.org:2546921</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://opensource.org/licenses/MIT</dc:rights>
  <dc:title>Rotation Equivariant CNNs for Digital Pathology</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>
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