@InProceedings{GamaOlivSant:2021:LeSeMe,
               author = "Gama, Pedro Henrique Targino and Oliveira, Hugo and Santos, 
                         Jefersson Alex dos",
          affiliation = "Universidade Federal de Minas Gerais, Brazil  and Universidade de 
                         S{\~a}o Paulo, Brazil  and Universidade Federal de Minas Gerais, 
                         Brazil",
                title = "Learning to Segment Medical Images from Few-Shot Sparse Labels",
            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 = "computer vision, meta-learning, semantic segmentation, medical 
                         imaging.",
             abstract = "In this paper, we propose a novel approach for few-shot semantic 
                         segmentation with sparse labeled images.We investigate the 
                         effectiveness of our method, which is based on the Model-Agnostic 
                         Meta-Learning (MAML) algorithm, in the medical scenario, where the 
                         use of sparse labeling and few-shot can alleviate the cost of 
                         producing new annotated datasets. Our method uses sparse labels in 
                         the meta-training and dense labels in the meta-test, thus making 
                         the model learn to predict dense labels from sparse ones. We 
                         conducted experiments with four Chest X-Ray datasets to evaluate 
                         two types of annotations (grid and points). The results show that 
                         our method is the most suitable when the target domain highly 
                         differs from source domains, achieving Jaccard scores comparable 
                         to dense labels, using less than 2% of the pixels of an image with 
                         labels in few-shot scenarios.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00021",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45EEKSE",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45EEKSE",
           targetfile = "SIBGRAPI_MetaLearning_Medical.pdf",
        urlaccessdate = "2024, May 06"
}