Fully convolutional networks in multimodal nonlinear microscopy images for automated detection of head and neck carcinoma: Pilot study.

Journal: Head & neck
Published Date: Jan 1, 2019

Abstract

BACKGROUND: A fully convolutional neural networks (FCN)-based automated image analysis algorithm to discriminate between head and neck cancer and noncancerous epithelium based on nonlinear microscopic images was developed.

Authors

  • Erik Rodner
    Computer Vision Group, Friedrich-Schiller-Universität Jena, Jena, Germany.
  • Thomas Bocklitz
    Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Jena, Germany.
  • Ferdinand von Eggeling
    Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Jena, Germany.
  • Günther Ernst
    Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany.
  • Olga Chernavskaia
    Leibniz Institute of Photonic Technology, Jena, Germany.
  • Jürgen Popp
    Leibniz Institute of Photonic Technology, Albert-Einstein-Str. 9, 07745 Jena, Germany. christoph.krafft@leibniz-ipht.de iwan.schie@leibniz-ipht.de and Institute of Physical Chemistry & Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany.
  • Joachim Denzler
    Computer Vision Group, Friedrich-Schiller-Universität Jena, Jena, Germany.
  • Orlando Guntinas-Lichius
    Department of Otorhinolaryngology, Jena University Hospital, Friedrich-Schiller-University Jena, 07747, Jena, Germany.

Keywords

External Resources

Popular Topics
Recent Journals