Authors:
Valeria Sorgente
1
;
Ilenia Verrillo
1
;
Mario Cesarelli
2
;
Antonella Santone
1
;
Fabio Martinelli
3
and
Francesco Mercaldo
1
Affiliations:
1
Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, Campobasso, Italy
;
2
Department of Engineering, University of Sannio, Benevento, Italy
;
3
Institute for High Performance Computing and Networking, National Research Council of Italy (CNR), Rende (CS), Italy
Keyword(s):
GAN, Generative Adversarial Networks, Bioimage, Deep Learning, Classification.
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 r
ealistic 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.
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