%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2021/09.06.13.19
%2 sid.inpe.br/sibgrapi/2021/09.06.13.19.08
%T Unsupervised Brain Anomaly Detection in MR Images
%D 2021
%A Martins, Samuel Botter,
%A Telea, Alexandru Cristian,
%A Falcão, Alexandre Xavier,
%@affiliation Federal Institute of São Paulo, Brazil
%@affiliation Utrecht University, Netherlands
%@affiliation University of Campinas, Brazil
%E Paiva, Afonso,
%E Menotti, David,
%E Baranoski, Gladimir V. G.,
%E Proença, Hugo Pedro,
%E Junior, Antonio Lopes Apolinario,
%E Papa, João Paulo,
%E Pagliosa, Paulo,
%E dos Santos, Thiago Oliveira,
%E e Sá, Asla Medeiros,
%E da Silveira, Thiago Lopes Trugillo,
%E Brazil, Emilio Vital,
%E Ponti, Moacir A.,
%E Fernandes, Leandro A. F.,
%E Avila, Sandra,
%B Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)
%C Gramado, RS, Brazil (virtual)
%8 18-22 Oct. 2021
%I Sociedade Brasileira de Computação
%J Porto Alegre
%S Proceedings
%K brain anomaly detection, unsupervised learning, outlier detection.
%X 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.
%@language en
%3 samuelmartins-paper-wtd-sigbrapi.pdf