@InProceedings{BenvenutoCasa:2023:ReImRe,
               author = "Benvenuto, Giovana Augusta and Casaca, Wallace",
          affiliation = "UNESP and UNESP",
                title = "Retinal images registration via unsupervised deep learning",
            booktitle = "Proceedings...",
                 year = "2023",
               editor = "Clua, Esteban Walter Gonzalez and K{\"o}rting, Thales Sehn and 
                         Paulovich, Fernando Vieira and Feris, Rogerio",
         organization = "Conference on Graphics, Patterns and Images, 36. (SIBGRAPI)",
             keywords = "Image registration, image processing, deep learning, retina.",
             abstract = "In ophthalmology and vision science applications, aligning a pair 
                         of retinal images is of paramount importance to support disease 
                         diagnosis and routine eye examinations. This paper introduces an 
                         end-to-end framework capable of learning the registration task in 
                         a fully unsupervised manner. The proposed approach combines 
                         Convolutional Neural Networks and Spatial Transformer Network into 
                         a unified pipeline that incorporates a similarity metric to gauge 
                         the difference between the images, enabling image alignment 
                         without requiring any ground-truth data. The validation study 
                         demonstrates that the model can successfully deal with several 
                         categories of fundus images, surpassing other recent techniques 
                         for retinal registration.",
  conference-location = "Rio Grande, RS",
      conference-year = "Nov. 06-09, 2023",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/49S63PP",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/49S63PP",
           targetfile = "Benvenuto_CRWTD_Sibigrapi2023.pdf",
        urlaccessdate = "2024, Apr. 27"
}