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.
The work is partially funded by an EU MC ITN TRANSACT 2012 (316679) project and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 690974. Authors thank the Nvidia GPU Education Center of the University of Calabria for the kind support.
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Notes
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Due to space constraints we omit a detailed description of Laplacian pyramid; we refer the reader to [1].
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Calimeri, F., Marzullo, A., Stamile, C., Terracina, G. (2017). Biomedical Data Augmentation Using Generative Adversarial Neural Networks. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_71
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