@InProceedings{OliveiraCesaGamaSant:2022:DoGeMe,
               author = "Oliveira, Hugo Neves de and Cesar Junior, Roberto Marcondes and 
                         Gama, Pedro Henrique Targino and Santos, Jefersson Alex dos",
          affiliation = "{Institute of Mathematics and Statistics - USP} and {Institute of 
                         Mathematics and Statistics - USP} and {Departamento de 
                         Ci{\^e}ncia da Computa{\c{c}}{\~a}o - UFMG} and {Computing 
                         Science and Mathematics - University of Stirling}",
                title = "Domain Generalization in Medical Image Segmentation via 
                         Meta-Learners",
            booktitle = "Proceedings...",
                 year = "2022",
         organization = "Conference on Graphics, Patterns and Images, 35. (SIBGRAPI)",
             keywords = "meta-learning, few-shot learning, semantic segmentation, medical 
                         imaging, domain generalization.",
             abstract = "Automatic and semi-automatic radiological image segmentation can 
                         help physicians in the processing of real-world medical data for 
                         several tasks such as detection/diagnosis of diseases and surgery 
                         planning. Current segmentation methods based on neural networks 
                         are highly data-driven, often requiring hundreds of laborious 
                         annotations to properly converge. The generalization capabilities 
                         of traditional supervised deep learning are also limited by the 
                         insufficient variability present in the training dataset. One very 
                         proliferous research field that aims to alleviate this dependence 
                         on large numbers of labeled data is Meta-Learning. Meta-Learning 
                         aims to improve the generalization capabilities of traditional 
                         supervised learning by training models to learn in a label 
                         efficient manner. In this tutorial we present an overview of the 
                         literature and proposed ways of merging this body of knowledge 
                         with deep segmentation architectures to produce highly adaptable 
                         multi-task meta-models for few-shot weakly-supervised semantic 
                         segmentation. We introduce a taxonomy to categorize Meta-Learning 
                         methods for both classification and segmentation, while also 
                         discussing how to adapt potentially any few-shot meta-learner to a 
                         weakly-supervised segmentation task.",
  conference-location = "Natal, RN",
      conference-year = "24-27 Oct. 2022",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/47MLCG8",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/47MLCG8",
           targetfile = "SIBGRAPI_2022_Oliveira_Meta-Learning.pdf",
        urlaccessdate = "2024, May 05"
}