{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T10:54:33Z","timestamp":1778151273173,"version":"3.51.4"},"reference-count":20,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T00:00:00Z","timestamp":1750982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EU DUCA, EU CyberSecPro, SYNAPSE, PTR 22-24 P2.01 (Cybersecurity) and SERICS","award":["PE00000014"],"award-info":[{"award-number":["PE00000014"]}]},{"name":"EU DUCA, EU CyberSecPro, SYNAPSE, PTR 22-24 P2.01 (Cybersecurity) and SERICS","award":["FSC 2014-2020"],"award-info":[{"award-number":["FSC 2014-2020"]}]},{"name":"EU DUCA, EU CyberSecPro, SYNAPSE, PTR 22-24 P2.01 (Cybersecurity) and SERICS","award":["D33B22000060001"],"award-info":[{"award-number":["D33B22000060001"]}]},{"name":"EU DUCA, EU CyberSecPro, SYNAPSE, PTR 22-24 P2.01 (Cybersecurity) and SERICS","award":["N.P2022WYAEW"],"award-info":[{"award-number":["N.P2022WYAEW"]}]},{"name":"Health Operational Plan","award":["PE00000014"],"award-info":[{"award-number":["PE00000014"]}]},{"name":"Health Operational Plan","award":["FSC 2014-2020"],"award-info":[{"award-number":["FSC 2014-2020"]}]},{"name":"Health Operational Plan","award":["D33B22000060001"],"award-info":[{"award-number":["D33B22000060001"]}]},{"name":"Health Operational Plan","award":["N.P2022WYAEW"],"award-info":[{"award-number":["N.P2022WYAEW"]}]},{"name":"PRIN-MUR-Ministry of Health, Progetto MolisCTe, Ministero delle Imprese e del Made in Italy","award":["PE00000014"],"award-info":[{"award-number":["PE00000014"]}]},{"name":"PRIN-MUR-Ministry of Health, Progetto MolisCTe, Ministero delle Imprese e del Made in Italy","award":["FSC 2014-2020"],"award-info":[{"award-number":["FSC 2014-2020"]}]},{"name":"PRIN-MUR-Ministry of Health, Progetto MolisCTe, Ministero delle Imprese e del Made in Italy","award":["D33B22000060001"],"award-info":[{"award-number":["D33B22000060001"]}]},{"name":"PRIN-MUR-Ministry of Health, Progetto MolisCTe, Ministero delle Imprese e del Made in Italy","award":["N.P2022WYAEW"],"award-info":[{"award-number":["N.P2022WYAEW"]}]},{"name":"FORESEEN: FORmal mEthodS for attack dEtEction in autonomous driviNg systems","award":["PE00000014"],"award-info":[{"award-number":["PE00000014"]}]},{"name":"FORESEEN: FORmal mEthodS for attack dEtEction in autonomous driviNg systems","award":["FSC 2014-2020"],"award-info":[{"award-number":["FSC 2014-2020"]}]},{"name":"FORESEEN: FORmal mEthodS for attack dEtEction in autonomous driviNg systems","award":["D33B22000060001"],"award-info":[{"award-number":["D33B22000060001"]}]},{"name":"FORESEEN: FORmal mEthodS for attack dEtEction in autonomous driviNg systems","award":["N.P2022WYAEW"],"award-info":[{"award-number":["N.P2022WYAEW"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/jimaging11070214","type":"journal-article","created":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T06:18:36Z","timestamp":1751264316000},"page":"214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study"],"prefix":"10.3390","volume":"11","author":[{"given":"Valeria","family":"Sorgente","sequence":"first","affiliation":[{"name":"Department of Medicine and Health Sciences \u201cVincenzo Tiberio\u201d, University of Molise, 86100 Campobasso, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dante","family":"Biagiucci","sequence":"additional","affiliation":[{"name":"Department of Medicine and Health Sciences \u201cVincenzo Tiberio\u201d, University of Molise, 86100 Campobasso, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mario","family":"Cesarelli","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Sannio, 82100 Benevento, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luca","family":"Brunese","sequence":"additional","affiliation":[{"name":"Department of Medicine and Health Sciences \u201cVincenzo Tiberio\u201d, University of Molise, 86100 Campobasso, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonella","family":"Santone","sequence":"additional","affiliation":[{"name":"Department of Medicine and Health Sciences \u201cVincenzo Tiberio\u201d, University of Molise, 86100 Campobasso, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabio","family":"Martinelli","sequence":"additional","affiliation":[{"name":"Institute for High Performance Computing and Networking, National Research Council of Italy, 87036 Rende, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9425-1657","authenticated-orcid":false,"given":"Francesco","family":"Mercaldo","sequence":"additional","affiliation":[{"name":"Department of Medicine and Health Sciences \u201cVincenzo Tiberio\u201d, University of Molise, 86100 Campobasso, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1080\/13645706.2019.1575882","article-title":"Introduction to artificial intelligence in medicine","volume":"28","author":"Mintz","year":"2019","journal-title":"Minim. 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