Barrett’s Esophagus Analysis Using Convolutional Neural Networks

  • 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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Metadaten
Author:Robert Mendel, Alanna EbigboORCiD, Andreas Probst, Helmut Messmann, Christoph PalmORCiDGND
DOI:https://doi.org/10.1007/978-3-662-54345-0_23
Parent Title (German):Bildverarbeitung für die Medizin 2017; Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 12. bis 14. März 2017 in Heidelberg
Publisher:Springer
Place of publication:Berlin
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2017
Release Date:2019/12/20
GND Keyword:Speiseröhrenkrebs; Diagnose; Maschinelles Lernen; Bilderkennung; Automatische Klassifikation
First Page:80
Last Page:85
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Biomedical Engineering - RCBE
Regensburg Medical Image Computing - ReMIC
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke