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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45EEKSE
Repositorysid.inpe.br/sibgrapi/2021/09.15.23.44
Last Update2021:09.29.12.59.33 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.15.23.44.20
Metadata Last Update2022:06.14.00.00.34 (UTC) administrator
DOI10.1109/SIBGRAPI54419.2021.00021
Citation KeyGamaOlivSant:2021:LeSeMe
TitleLearning to Segment Medical Images from Few-Shot Sparse Labels
FormatOn-line
Year2021
Access Date2025, Mar. 13
Number of Files1
Size2105 KiB
2. Context
Author1 Gama, Pedro Henrique Targino
2 Oliveira, Hugo
3 Santos, Jefersson Alex dos
Affiliation1 Universidade Federal de Minas Gerais, Brazil
2 Universidade de São Paulo, Brazil
3 Universidade Federal de Minas Gerais, 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 Addresspehtg13@gmail.com
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2021-09-29 12:59:33 :: pehtg13@gmail.com -> administrator :: 2021
2022-03-02 00:54:16 :: administrator -> menottid@gmail.com :: 2021
2022-03-02 13:26:24 :: menottid@gmail.com -> administrator :: 2021
2022-06-14 00:00:34 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordscomputer vision
meta-learning
semantic segmentation
medical imaging
AbstractIn this paper, we propose a novel approach for few-shot semantic segmentation with sparse labeled images.We investigate the effectiveness of our method, which is based on the Model-Agnostic Meta-Learning (MAML) algorithm, in the medical scenario, where the use of sparse labeling and few-shot can alleviate the cost of producing new annotated datasets. Our method uses sparse labels in the meta-training and dense labels in the meta-test, thus making the model learn to predict dense labels from sparse ones. We conducted experiments with four Chest X-Ray datasets to evaluate two types of annotations (grid and points). The results show that our method is the most suitable when the target domain highly differs from source domains, achieving Jaccard scores comparable to dense labels, using less than 2% of the pixels of an image with labels in few-shot scenarios.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2021 > Learning to Segment...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Learning to Segment...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45EEKSE
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45EEKSE
Languageen
Target FileSIBGRAPI_MetaLearning_Medical.pdf
User Grouppehtg13@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 100
sid.inpe.br/sibgrapi/2022/06.10.21.49 2
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 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 volume


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