@InProceedings{AlmeidaPerRenCavSij:2021:ApDeLo,
               author = "Almeida, Mateus Baltazar and Pereira, Luis F. Alves and Ren, Tsang 
                         Ing and Cavalcanti, George D. C. and Sijbers, Jan",
          affiliation = "{Universidade Federal do Agreste de Pernambuco  } and 
                         {Universidade Federal do Agreste de Pernambuco  } and 
                         {Universidade Federal de Pernambuco  } and {Universidade Federal 
                         de Pernambuco  } and {University of Antwerp}",
                title = "The gated recurrent conditional generative adversarial network 
                         (GRC-GAN): application to denoising of low-dose CT images",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "adversarial networks, gated unit, denoising.",
             abstract = "The ionizing radiation that propagates through the human body at 
                         Computed Tomography (CT) exams is known to be carcinogenic. For 
                         this reason, the development of methods for image reconstruction 
                         that operate with reduced radiation doses is essential. If we 
                         reduce the electrical current in the electrically powered X-ray 
                         tubes of CT scanners, the amount of radiation that passes through 
                         the human body during a CT exam is reduced. However, significant 
                         image noise emerges in the reconstructed CT slices if standard 
                         reconstruction methods are applied. To estimate routine-dose CT 
                         images from low-dose CT images and thus reduce noise, the 
                         Conditional Generative Adversarial Network (cGAN) was recently 
                         proposed in the literature. In this work, we introduce the Gated 
                         Recurrent Conditional Generative Adversarial Network (GRC-GAN) 
                         that is based on the usage of network gates to learn the specific 
                         regions of the input image to be updated using the cGAN denoising 
                         operation. Moreover, the GRC-GAN is executed recurrently in 
                         multiple time steps. At each time step, different parts of the 
                         input image are denoised. As a result, our GRC-GAN better focus on 
                         the denoise criterium than the regular cGAN in the LoDoPaB-CT 
                         benchmark.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00026",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00026",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45D2P52",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45D2P52",
           targetfile = "40.pdf",
        urlaccessdate = "2024, Apr. 27"
}