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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45CT8PH
Repositorysid.inpe.br/sibgrapi/2021/09.06.13.19
Last Update2021:09.06.13.19.08 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.06.13.19.08
Metadata Last Update2022:09.10.00.16.17 (UTC) administrator
Citation KeyMartinsTeleFalc:2021:UnBrAn
TitleUnsupervised Brain Anomaly Detection in MR Images
FormatOn-line
Year2021
Access Date2024, May 06
Number of Files1
Size3615 KiB
2. Context
Author1 Martins, Samuel Botter
2 Telea, Alexandru Cristian
3 Falcão, Alexandre Xavier
Affiliation1 Federal Institute of São Paulo, Brazil
2 Utrecht University, Netherlands
3 University of Campinas, Brazil
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addresssamuel.martins@ifsp.edu.br
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
History (UTC)2021-09-06 13:19:08 :: samuel.martins@ifsp.edu.br -> administrator ::
2022-09-10 00:16:17 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsbrain anomaly detection
unsupervised learning
outlier detection
AbstractMany 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.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2021 > Unsupervised Brain Anomaly...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45CT8PH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45CT8PH
Languageen
Target Filesamuelmartins-paper-wtd-sigbrapi.pdf
User Groupsamuel.martins@ifsp.edu.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 3
sid.inpe.br/banon/2001/03.30.15.38.24 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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