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Elena Loli Piccolomini, Marco Prato, Margherita Scipione and Andrea Sebastiani
In this paper, we propose a new deep learning approach based on unfolded neural networks for the reconstruction of X-ray computed tomography images from few views. We start from a model-based approach in a compressed sensing framework, described by the m...
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Carmelo Scribano, Danilo Pezzi, Giorgia Franchini and Marco Prato
With the recent advancements in the field of diffusion generative models, it has been shown that defining the generative process in the latent space of a powerful pretrained autoencoder can offer substantial advantages. This approach, by abstracting away...
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Giorgia Franchini, Micaela Verucchi, Ambra Catozzi, Federica Porta and Marco Prato
It is well known that biomedical imaging analysis plays a crucial role in the healthcare sector and produces a huge quantity of data. These data can be exploited to study diseases and their evolution in a deeper way or to predict their onsets. In particu...
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