@InProceedings{MartinsTeleFalc:2021:UnBrAn,
               author = "Martins, Samuel Botter and Telea, Alexandru Cristian and 
                         Falc{\~a}o, Alexandre Xavier",
          affiliation = "Federal Institute of S{\~a}o Paulo, Brazil and Utrecht 
                         University, Netherlands and University of Campinas, Brazil",
                title = "Unsupervised Brain Anomaly Detection in MR 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 = "brain anomaly detection, unsupervised learning, outlier 
                         detection.",
             abstract = "Many brain anomalies are associated with abnormal asymmetries. To 
                         detect and/or segment such anomalies in brain images, most 
                         automatic methods rely on supervised learning. This requires a 
                         large number of high-quality annotated training images, which is 
                         lacking for most medical image analysis problems. In contrast, 
                         unsupervised methods aim to learn a model from unlabeled healthy 
                         images, so that an unseen image that breaks priors of this model, 
                         i.e., an outlier, is considered an anomaly. This paper addresses 
                         the development of solutions to leverage unsupervised machine 
                         learning for the detection/analysis of abnormal brain asymmetries 
                         related to anomalies in magnetic resonance (MR) images. 
                         Experimental results on 3D MR-T1 images from healthy subjects and 
                         patients with a variety of lesions show the effectiveness and 
                         robustness of the proposed unsupervised approaches for brain 
                         anomaly detection.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CT8PH",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CT8PH",
           targetfile = "samuelmartins-paper-wtd-sigbrapi.pdf",
        urlaccessdate = "2024, May 06"
}