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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45EH5HE
Repositorysid.inpe.br/sibgrapi/2021/09.16.13.14
Last Update2021:09.16.13.14.37 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.16.13.14.37
Metadata Last Update2022:09.10.00.16.17 (UTC) administrator
Citation KeyOliveiraAraúSant:2021:SeSeMu
TitleSemantic Segmentation with Multi-Source Domain Adaptation for Radiological Images
FormatOn-line
Year2021
Access Date2024, Apr. 19
Number of Files1
Size2936 KiB
2. Context
Author1 Oliveira, Hugo Neves de
2 Araújo, Arnaldo de Albuquerque
3 Santos, Jefersson Alex dos
Affiliation1 Departamento de Ciência da Computação - UFMG
2 Departamento de Ciência da Computação - UFMG
3 Departamento de Ciência da Computação - UFMG
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 Addressoliveirahugo@dcc.ufmg.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)2021-09-16 13:14:37 :: oliveirahugo@dcc.ufmg.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
Keywordsdomain generalization
biomedical images
generative adversarial networks
image-to-image translation
AbstractDifferences in digitization equipment and techniques in radiology may hamper the use of data-driven deep learning approaches. In order to mitigate this limitation, in this work we merge generative image translation networks with supervised semantic segmentation architectures, yielding two semi-supervised methods for domain adaptation in medical images. We compare our methods with traditional baselines in the literature using 3 image domains, 16 datasets and 8 segmentation tasks organized into three sets of experiments. Analysis of the results showed that the proposed methods for Domain Adaptation often reached Jaccard scores of 0.9 or higher in unsupervised or semi-supervised settings. We observe that unsupervised domain adaptation performance is close to the performance of fully supervised adaptation in most cases, bridging an important gap in the efficacy of neural networks between labeled and unlabeled datasets.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2021 > Semantic Segmentation with...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45EH5HE
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45EH5HE
Languageen
Target FileWTD_SIBGRAPI_2021_Final.pdf
User Groupoliveirahugo@dcc.ufmg.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 4
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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