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Feasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks

  • Nils GessertEmail author
  • Lukas Wittig
  • Daniel Drömann
  • Tobias Keck
  • Alexander Schlaefer
  • David B. Ellebrecht
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Histological evaluation of tissue samples is a typical approach to identify colorectal cancer metastases in the peritoneum. For immediate assessment, reliable and real-time in-vivo imaging would be required. For example, intraoperative confocal laser microscopy has been shown to be suitable for distinguishing organs and also malignant and benign tissue. So far, the analysis is done by human experts. We investigate the feasibility of automatic colon cancer classification from confocal laser microscopy images using deep learning models. We overcome very small dataset sizes through transfer learning with state-of-the-art architectures. We achieve an accuracy of 89:1% for cancer detection in the peritoneum which indicates viability as an intraoperative decision support system.

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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Nils Gessert
    • 1
    Email author
  • Lukas Wittig
    • 2
  • Daniel Drömann
    • 2
  • Tobias Keck
    • 3
  • Alexander Schlaefer
    • 1
  • David B. Ellebrecht
    • 3
  1. 1.Institute of Medical TechnologyHamburg University of TechnologyHamburgDeutschland
  2. 2.Department of PulmologyUniversity Medical Centre Schleswig-HolsteinKielDeutschland
  3. 3.Department of SurgeryUniversity Medical Centre Schleswig-HolsteinKielDeutschland

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