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Barrett’s Esophagus Analysis Using Convolutional Neural Networks

  • Robert Mendel
  • Alanna Ebigbo
  • Andreas Probst
  • Helmut Messmann
  • Christoph Palm
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Abstract

We propose an automatic approach for early detection of adenocarcinoma in the esophagus. High-definition endoscopic images (50 cancer, 50 Barrett) are partitioned into a dataset containing approximately equal amounts of patches showing cancerous and non-cancerous regions. A deep convolutional neural network is adapted to the data using a transfer learning approach. The final classification of an image is determined by at least one patch, for which the probability being a cancer patch exceeds a given threshold. The model was evaluated with leave one patient out cross-validation. With sensitivity and specificity of 0.94 and 0.88, respectively, our findings improve recently published results on the same image data base considerably. Furthermore, the visualization of the class probabilities of each individual patch indicates, that our approach might be extensible to the segmentation domain.

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Copyright information

© Springer-Verlag GmbH Deutschland 2017

Authors and Affiliations

  • Robert Mendel
    • 1
  • Alanna Ebigbo
    • 2
  • Andreas Probst
    • 2
  • Helmut Messmann
    • 2
  • Christoph Palm
    • 1
    • 3
  1. 1.Regensburg Medical Image Computing (ReMIC)Ostbayerische Technische Hochschule Regensburg (OTH Regensburg)RegensburgDeutschland
  2. 2.III. Medizinische KlinikKlinikum AugsburgAugsburgDeutschland
  3. 3.Regensburg Center of Biomedical Engineering (RCBE)OTH Regensburg and Regensburg UniversityErlangen-NürnbergDeutschland

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