Barrett's esophagus analysis using infinity Restricted Boltzmann Machines

  • The number of patients with Barret’s esophagus (BE) has increased in the last decades. Considering the dangerousness of the disease and its evolution to adenocarcinoma, an early diagnosis of BE may provide a high probability of cancer remission. However, limitations regarding traditional methods of detection and management of BE demand alternative solutions. As such, computer-aided tools have been recently used to assist in this problem, but the challenge still persists. To manage the problem, we introduce the infinity Restricted Boltzmann Machines (iRBMs) to the task of automatic identification of Barrett’s esophagus from endoscopic images of the lower esophagus. Moreover, since iRBM requires a proper selection of its meta-parameters, we also present a discriminative iRBM fine-tuning using six meta-heuristic optimization techniques. We showed that iRBMs are suitable for the context since it provides competitive results, as well as the meta-heuristic techniques showed to be appropriate for such task.

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Author:Leandro A. Passos, Luis Antonio De Souza Jr.ORCiD, Robert MendelORCiD, Alanna EbigboORCiD, Andreas Probst, Helmut Messmann, Christoph PalmORCiDGND, João Paulo PapaORCiD
Parent Title (English):Journal of Visual Communication and Image Representation
Document Type:Article
Year of first Publication:2019
Release Date:2019/12/18
Tag:Barrett’s esophagus; Deep learning; Infinity Restricted Boltzmann Machines; Meta-heuristics
GND Keyword:Speiseröhrenkrankheit; Diagnose; Boltzmann-Maschine; Metaheuristik; Maschinelles Lernen
First Page:475
Last Page:485
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Health Sciences and Technology - RCHST
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