Bioimages Synthesis and Detection Through Generative Adversarial Network: A Multi-Case Study

Valeria Sorgente, Ilenia Verrillo, Mario Cesarelli, Antonella Santone, Fabio Martinelli, Francesco Mercaldo

2025

Abstract

The rapid advancement of Generative Adversarial Networks technology raises ethical and security concerns, emphasizing the need for guidelines and measures to prevent misuse. Strengthening systems to differentiate real from synthetic images and ensuring responsible application in clinical settings could address data scarcity in the biomedical field. For these reasons, considering the increasing popularity of the possibility to generate synthetic images by exploiting artificial intelligence, we investigate the application of Generative Adversarial Networks to generate realistic synthetic bioimages for common pathology representations. We propose a method consisting of two steps: the first one is related to the training of a Deep Convolutional Generative Adversarial Network, while the second step is represented by the evaluation of the bioimages quality using classification-based metrics, comparing synthetic and real images. The model demonstrated promising results, achieving visually realistic images for datasets such as PathMNIST and RetinaMNIST, with accuracy improving over training epochs. However, challenges arose with datasets like ChestMNIST and OCTMNIST, where image quality was limited, showing poor detail and distinguishability from real samples.

Paper Citation

in Harvard Style

Sorgente V., Verrillo I., Cesarelli M., Santone A., Martinelli F. and Mercaldo F. (2025). Bioimages Synthesis and Detection Through Generative Adversarial Network: A Multi-Case Study. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING; ISBN 978-989-758-731-3, SciTePress, pages 332-339. DOI: 10.5220/0013228900003911

in Bibtex Style

@conference{bioimaging25,
author={Valeria Sorgente and Ilenia Verrillo and Mario Cesarelli and Antonella Santone and Fabio Martinelli and Francesco Mercaldo},
title={Bioimages Synthesis and Detection Through Generative Adversarial Network: A Multi-Case Study},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING},
year={2025},
pages={332-339},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013228900003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING
TI - Bioimages Synthesis and Detection Through Generative Adversarial Network: A Multi-Case Study
SN - 978-989-758-731-3
AU - Sorgente V.
AU - Verrillo I.
AU - Cesarelli M.
AU - Santone A.
AU - Martinelli F.
AU - Mercaldo F.
PY - 2025
SP - 332
EP - 339
DO - 10.5220/0013228900003911
PB - SciTePress