Endoscopy-driven pretraining for classification of dysplasia in barrett's esophagus with endoscopic narrow-band imaging zoom videos

Joost van der Putten (Corresponding author), Maarten Struyvenberg, Jeroen de Groof, Wouter Curvers, Erik Schoon, Francisco Baldaque-Silva, Jacques Bergman, Fons van der Sommen, Peter H.N. de With

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

Endoscopic diagnosis of early neoplasia in Barrett's Esophagus is generally a two-step process of primary detection in overview, followed by detailed inspection of any visible abnormalities using Narrow Band Imaging (NBI). However, endoscopists struggle with evaluating NBI-zoom imagery of subtle abnormalities. In this work, we propose the first results of a deep learning system for the characterization of NBI-zoom imagery of Barrett's Esophagus with an accuracy, sensitivity, and specificity of 83.6%, 83.1%, and 84.0%, respectively. We also show that endoscopy-driven pretraining outperforms two models, one without pretraining as well as a model with ImageNet initialization. The final model outperforms absence of pretraining by approximately 10% and the performance is 2% higher in terms of accuracy compared to ImageNet pretraining. Furthermore, the practical deployment of our model is not hampered by ImageNet licensing, thereby paving the way for clinical application.

Original languageEnglish
Article number3407
Number of pages11
JournalApplied Sciences
Volume10
Issue number10
DOIs
Publication statusPublished - 1 May 2020

Keywords

  • Barrett's esophagus
  • Classification
  • Deep learning
  • Endoscopic zoomimagery
  • Machine learning

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