Synthesizing photo-realistic images is a challenging problem with many practical applications [15]. In many cases, the availability of a significant amount of images is crucial, yet obtaining them might be not trivial. For instance, obtaining huge databases of images is hard, in the biomedical domain, but strictly needed in order to improve both algorithms and physicians’ skills. In the latest years, new deep learning models have been proposed in the literature, called Generative Adversarial Neural Networks (GANNs) [7], that turned out as effective at synthesizing high-quality image in several domains. In this work we propose a new application of GANNs to the automatic generation of artificial Magnetic Resonance Images (MRI) of slices of the human brain; both quantitative and human-based evaluations of generated images have been carried out in order to assess effectiveness of the method.

Biomedical Data Augmentation using Generative Adversarial Neural Networks

CALIMERI, Francesco;Marzullo A;Stamile C;Terracina G.
2017

Abstract

Synthesizing photo-realistic images is a challenging problem with many practical applications [15]. In many cases, the availability of a significant amount of images is crucial, yet obtaining them might be not trivial. For instance, obtaining huge databases of images is hard, in the biomedical domain, but strictly needed in order to improve both algorithms and physicians’ skills. In the latest years, new deep learning models have been proposed in the literature, called Generative Adversarial Neural Networks (GANNs) [7], that turned out as effective at synthesizing high-quality image in several domains. In this work we propose a new application of GANNs to the automatic generation of artificial Magnetic Resonance Images (MRI) of slices of the human brain; both quantitative and human-based evaluations of generated images have been carried out in order to assess effectiveness of the method.
MRI; Generative Adversarial Networks; Biomedical Imaging
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/182145
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