Dataset Open Access

Rotation Equivariant CNNs for Digital Pathology

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


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="URL">https://zenodo.org/record/2546921</identifier>
  <creators>
    <creator>
      <creatorName>B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, M. Welling</creatorName>
      <affiliation>University of Amsterdam</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Rotation Equivariant CNNs for Digital Pathology</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <dates>
    <date dateType="Issued">2018-09-26</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2546921</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1007/978-3-030-00934-2_24</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://opensource.org/licenses/MIT">MIT License</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;</description>
  </descriptions>
</resource>
2,658
16,481
views
downloads
Views 2,658
Downloads 16,481
Data volume 38.6 TB
Unique views 2,368
Unique downloads 4,072

Share

Cite as