@InProceedings{OliveiraAraúSant:2021:SeSeMu,
               author = "Oliveira, Hugo Neves de and Ara{\'u}jo, Arnaldo de Albuquerque 
                         and Santos, Jefersson Alex dos",
          affiliation = "{Departamento de Ci{\^e}ncia da Computa{\c{c}}{\~a}o - UFMG} 
                         and {Departamento de Ci{\^e}ncia da Computa{\c{c}}{\~a}o - 
                         UFMG} and {Departamento de Ci{\^e}ncia da Computa{\c{c}}{\~a}o 
                         - UFMG}",
                title = "Semantic Segmentation with Multi-Source Domain Adaptation for 
                         Radiological 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 = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "domain generalization, biomedical images, generative adversarial 
                         networks, image-to-image translation.",
             abstract = "Differences in digitization equipment and techniques in radiology 
                         may hamper the use of data-driven deep learning approaches. In 
                         order to mitigate this limitation, in this work we merge 
                         generative image translation networks with supervised semantic 
                         segmentation architectures, yielding two semi-supervised methods 
                         for domain adaptation in medical images. We compare our methods 
                         with traditional baselines in the literature using 3 image 
                         domains, 16 datasets and 8 segmentation tasks organized into three 
                         sets of experiments. Analysis of the results showed that the 
                         proposed methods for Domain Adaptation often reached Jaccard 
                         scores of 0.9 or higher in unsupervised or semi-supervised 
                         settings. We observe that unsupervised domain adaptation 
                         performance is close to the performance of fully supervised 
                         adaptation in most cases, bridging an important gap in the 
                         efficacy of neural networks between labeled and unlabeled 
                         datasets.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45EH5HE",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45EH5HE",
           targetfile = "WTD_SIBGRAPI_2021_Final.pdf",
        urlaccessdate = "2024, May 05"
}