Fine-tuning Generative Adversarial Networks using Metaheuristics
Channel: BVM Conference
Date: 25/06/2021

A Case Study on Barrett’s Esophagus Identiﬁcation.

Barrett's esophagus denotes a disorder in the digestive system
that affects the esophagus' mucosal cells, causing reux, 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 ne-tune GANs in the context of
Barrett's esophagus identication, as well as to investigate the feasibility
of generating high-quality synthetic images for early-cancer assisted
identication.

References:
Souza L.A. et al. (2021) Fine-tuning Generative Adversarial Networks using Metaheuristics. In: Palm C., Deserno T.M., Handels H., Maier A., Maier-Hein K., Tolxdorff T. (eds) Bildverarbeitung für die Medizin 2021. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-33198-6_50