Fine-tuning Generative Adversarial Networks using Metaheuristics

  • Barrett's esophagus denotes a disorder in the digestive system that affects the esophagus' mucosal cells, causing reflux, and showing potential convergence to esophageal adenocarcinoma if not treated in initial stages. Thus, fast and reliable computer-aided diagnosis becomes considerably welcome. Nevertheless, such approaches usually suffer from imbalanced datasets, which can be addressed through Generative Adversarial Networks (GANs). Such techniques generate realistic images based on observed samples, even though at the cost of a proper selection of its hyperparameters. Many works employed a class of nature-inspired algorithms called metaheuristics to tackle the problem considering distinct deep learning approaches. Therefore, this paper's main contribution is to introduce metaheuristic techniques to fine-tune GANs in the context of Barrett's esophagus identification, as well as to investigate the feasibility of generating high-quality synthetic images for early-cancer assisted identification.

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Metadaten
Author:Louis A. Souza, Leandro A. Passos, Robert Mendel, Alanna EbigboORCiD, Andreas Probst, Helmut Messmann, Christoph PalmORCiDGND, João Paulo PapaORCiD
DOI:https://doi.org/10.1007/978-3-658-33198-6_50
ISBN:978-3-658-33197-9
Parent Title (English):Bildverarbeitung für die Medizin 2021. Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021
Subtitle (English):A Case Study on Barrett's Esophagus Identification
Publisher:Springer Vieweg
Place of publication:Wiesbaden
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2021
Release Date:2021/03/10
GND Keyword:Endoskopie; Computerunterstützte Medizin; Deep Learning
First Page:205
Last Page:210
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Health Sciences and Technology - RCHST
Regensburg Medical Image Computing - ReMIC
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke
6 Technik, Medizin, angewandte Wissenschaften