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.
Author: | Luis Antonio de Souza Jr.ORCiD, Leandro A. Passos, Robert MendelORCiD, Alanna EbigboORCiD, Andreas Probst, Helmut Messmann, Christoph PalmORCiDGND, João Paulo PapaORCiD |
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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 | |
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC) | |
Begutachtungsstatus: | peer-reviewed |
research focus: | Lebenswissenschaften und Ethik |
Licence (German): | Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG |