van der Putten, Joost;
Struyvenberg, Maarten;
de Groof, Jeroen;
Curvers, Wouter;
Schoon, Erik;
Baldaque-Silva, Francisco;
Bergman, Jacques;
van der Sommen, Fons;
de With, Peter H.N.
Endoscopy-Driven Pretraining for Classification of Dysplasia in Barrett’s Esophagus with Endoscopic Narrow-Band Imaging Zoom Videos
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Media type:
E-Article
Title:
Endoscopy-Driven Pretraining for Classification of Dysplasia in Barrett’s Esophagus with Endoscopic Narrow-Band Imaging Zoom Videos
Contributor:
van der Putten, Joost;
Struyvenberg, Maarten;
de Groof, Jeroen;
Curvers, Wouter;
Schoon, Erik;
Baldaque-Silva, Francisco;
Bergman, Jacques;
van der Sommen, Fons;
de With, Peter H.N.
Published:
MDPI AG, 2020
Published in:
Applied Sciences, 10 (2020) 10, Seite 3407
Language:
English
DOI:
10.3390/app10103407
ISSN:
2076-3417
Origination:
Footnote:
Description:
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.