Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study
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 genera...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/7/214 |
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| author | Valeria Sorgente Dante Biagiucci Mario Cesarelli Luca Brunese Antonella Santone Fabio Martinelli Francesco Mercaldo |
| author_facet | Valeria Sorgente Dante Biagiucci Mario Cesarelli Luca Brunese Antonella Santone Fabio Martinelli Francesco Mercaldo |
| author_sort | Valeria Sorgente |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-efe7e87d294b4665adfd7257e744fe9a |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-efe7e87d294b4665adfd7257e744fe9a2025-08-20T03:36:21ZengMDPI AGJournal of Imaging2313-433X2025-06-0111721410.3390/jimaging11070214Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case StudyValeria Sorgente0Dante Biagiucci1Mario Cesarelli2Luca Brunese3Antonella Santone4Fabio Martinelli5Francesco Mercaldo6Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Engineering, University of Sannio, 82100 Benevento, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyInstitute for High Performance Computing and Networking, National Research Council of Italy, 87036 Rende, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyBackground: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.https://www.mdpi.com/2313-433X/11/7/214GANconvolutional generative adversarial networkDCGANdeep convolutional generative adversarial networkdeep learningbioimages |
| spellingShingle | Valeria Sorgente Dante Biagiucci Mario Cesarelli Luca Brunese Antonella Santone Fabio Martinelli Francesco Mercaldo Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study Journal of Imaging GAN convolutional generative adversarial network DCGAN deep convolutional generative adversarial network deep learning bioimages |
| title | Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study |
| title_full | Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study |
| title_fullStr | Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study |
| title_full_unstemmed | Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study |
| title_short | Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study |
| title_sort | exploring bioimage synthesis and detection via generative adversarial networks a multi faceted case study |
| topic | GAN convolutional generative adversarial network DCGAN deep convolutional generative adversarial network deep learning bioimages |
| url | https://www.mdpi.com/2313-433X/11/7/214 |
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