Analysis of Celiac Disease with Multimodal Deep Learning

  • Celiac disease is an autoimmune disorder caused by gluten that results in an inflammatory response of the small intestine.We investigated whether celiac disease can be detected using endoscopic images through a deep learning approach. The results show that additional clinical parameters can improve the classification accuracy. In this work, we distinguished between healthy tissue and Marsh III, according to the Marsh score system. We first trained a baseline network to classify endoscopic images of the small bowel into these two classes and then augmented the approach with a multimodality component that took the antibody status into account.

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Author:David Rauber, Robert Mendel, Markus W. ScheppachORCiD, Alanna EbigboORCiD, Helmut Messmann, Christoph PalmORCiDGND
Parent Title (English):Bildverarbeitung für die Medizin 2022: Proceedings, German Workshop on Medical Image Computing, Heidelberg, June 26-28, 2022
Publisher:Springer Vieweg
Place of publication:Wiesbaden
Document Type:conference proceeding (article)
Year of first Publication:2022
Release Date:2022/04/06
Tag:Deep Learning; Endoscopy
First Page:115
Last Page:120
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Biomedical Engineering - RCBE
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
research focus:Lebenswissenschaften und Ethik
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG