Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study
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.
Authors
- Antonella Santone (ORCID: https://orcid.org/0000-0002-2634-4456)
- Francesco Mercaldo (ORCID: https://orcid.org/0000-0002-9425-1657)
- Luca Brunese (ORCID: https://orcid.org/0000-0002-4666-0544)
- Fabio Martinelli
- Mario Cesarelli (ORCID: https://orcid.org/0000-0001-9068-313X)
- Valeria Sorgente
- Dante Biagiucci
Institutions
- University of Molise (IT)
- University of Sannio (IT)
- Institute for High Performance Computing and Networking (IT)
- National Research Council (IT)
Publication Details
- Journal
- Journal of Imaging
- Published
- 2025-06-27
- DOI
- https://doi.org/10.3390/jimaging11070214
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Istituto Nazionale per l'Assicurazione Contro Gli Infortuni sul Lavoro