Previous Article in Journal
Iterative Reconstruction with Dynamic ElasticNet Regularization for Nuclear Medicine Imaging
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
J. Imaging
Volume 11
Issue 7
10.3390/jimaging11070214
jimaging-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study

by
Valeria Sorgente
1,*,
Dante Biagiucci
1,
Mario Cesarelli
2,
Luca Brunese
1,
Antonella Santone
1,
Fabio Martinelli
3 and
Francesco Mercaldo
1,*
1
Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
2
Department of Engineering, University of Sannio, 82100 Benevento, Italy
3
Institute for High Performance Computing and Networking, National Research Council of Italy, 87036 Rende, Italy
*
Authors to whom correspondence should be addressed.
J. Imaging 2025, 11(7), 214; https://doi.org/10.3390/jimaging11070214 (registering DOI)
Submission received: 29 April 2025 / Revised: 13 June 2025 / Accepted: 25 June 2025 / Published: 27 June 2025
(This article belongs to the Section Medical Imaging)
Versions Notes

Abstract

Background: Generative Adversarial Networks (GANs), thanks to their great versatility, have a plethora of applications in biomedical imaging with the goal of simulating complex pathological conditions and creating clinical data used for training advanced machine learning models. The ability to generate high-quality synthetic clinical data not only addresses issues related to the scarcity of annotated bioimages but also supports the continuous improvement of diagnostic tools. Method: We propose a two-step method aimed to detect whether a bioimage can be considered fake or real. The first step is related to bioimage generation using a Deep Convolutional GAN, while the second step involves the training and testing of a set of machine learning models aimed to distinguish between real and generated bioimages. Results: We evaluate our approach by exploiting six different datasets. We observe notable results, demonstrating the ability of Deep Convolutional GAN to generate realistic synthetic images for some specific bioimages. However, for other bioimages, the accuracy does not align with the expected trend, indicating challenges in generating images that closely resemble real ones. Conclusions: This study highlights both the potential and limitations of GAN in generating realistic bioimages. Future work will focus on improving generation quality and detection accuracy across different datasets.
Keywords: GAN; convolutional generative adversarial network; DCGAN; deep convolutional generative adversarial network; deep learning; bioimages; classification GAN; convolutional generative adversarial network; DCGAN; deep convolutional generative adversarial network; deep learning; bioimages; classification

Share and Cite

MDPI and ACS Style

Sorgente, V.; Biagiucci, D.; Cesarelli, M.; Brunese, L.; Santone, A.; Martinelli, F.; Mercaldo, F. Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study. J. Imaging 2025, 11, 214. https://doi.org/10.3390/jimaging11070214

AMA Style

Sorgente V, Biagiucci D, Cesarelli M, Brunese L, Santone A, Martinelli F, Mercaldo F. Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study. Journal of Imaging. 2025; 11(7):214. https://doi.org/10.3390/jimaging11070214

Chicago/Turabian Style

Sorgente, Valeria, Dante Biagiucci, Mario Cesarelli, Luca Brunese, Antonella Santone, Fabio Martinelli, and Francesco Mercaldo. 2025. "Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study" Journal of Imaging 11, no. 7: 214. https://doi.org/10.3390/jimaging11070214

APA Style

Sorgente, V., Biagiucci, D., Cesarelli, M., Brunese, L., Santone, A., Martinelli, F., & Mercaldo, F. (2025). Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study. Journal of Imaging, 11(7), 214. https://doi.org/10.3390/jimaging11070214

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop