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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/47MLCG8
Repositorysid.inpe.br/sibgrapi/2022/09.27.12.01
Last Update2022:09.27.12.01.23 (UTC) oliveirahugo@dcc.ufmg.br
Metadata Repositorysid.inpe.br/sibgrapi/2022/09.27.12.01.24
Metadata Last Update2023:05.23.04.20.43 (UTC) administrator
Citation KeyOliveiraCesaGamaSant:2022:DoGeMe
TitleDomain Generalization in Medical Image Segmentation via Meta-Learners
Short TitleDomain Generalization in Medical Image Segmentation via Meta-Learners
FormatOn-line
Year2022
Access Date2024, May 05
Number of Files1
Size1999 KiB
2. Context
Author1 Oliveira, Hugo Neves de
2 Cesar Junior, Roberto Marcondes
3 Gama, Pedro Henrique Targino
4 Santos, Jefersson Alex dos
Affiliation1 Institute of Mathematics and Statistics - USP
2 Institute of Mathematics and Statistics - USP
3 Departamento de Ciência da Computação - UFMG
4 Computing Science and Mathematics - University of Stirling
e-Mail Addressoliveirahugo@ime.usp.br
Conference NameConference on Graphics, Patterns and Images, 35 (SIBGRAPI)
Conference LocationNatal, RN
Date24-27 Oct. 2022
Book TitleProceedings
Tertiary TypeTutorial
History (UTC)2022-09-30 23:38:46 :: oliveirahugo@dcc.ufmg.br -> administrator :: 2022
2023-05-23 04:20:43 :: administrator -> :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsmeta-learning
few-shot learning
semantic segmentation
medical imaging
domain generalization
AbstractAutomatic and semi-automatic radiological image segmentation can help physicians in the processing of real-world medical data for several tasks such as detection/diagnosis of diseases and surgery planning. Current segmentation methods based on neural networks are highly data-driven, often requiring hundreds of laborious annotations to properly converge. The generalization capabilities of traditional supervised deep learning are also limited by the insufficient variability present in the training dataset. One very proliferous research field that aims to alleviate this dependence on large numbers of labeled data is Meta-Learning. Meta-Learning aims to improve the generalization capabilities of traditional supervised learning by training models to learn in a label efficient manner. In this tutorial we present an overview of the literature and proposed ways of merging this body of knowledge with deep segmentation architectures to produce highly adaptable multi-task meta-models for few-shot weakly-supervised semantic segmentation. We introduce a taxonomy to categorize Meta-Learning methods for both classification and segmentation, while also discussing how to adapt potentially any few-shot meta-learner to a weakly-supervised segmentation task.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2022 > Domain Generalization in Medical Image Segmentation via Meta-Learners
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source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/47MLCG8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/47MLCG8
Languageen
Target FileSIBGRAPI_2022_Oliveira_Meta-Learning.pdf
User Groupoliveirahugo@dcc.ufmg.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/495MHJ8
Citing Item Listsid.inpe.br/sibgrapi/2023/05.19.12.10 7
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 editor electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session sponsor subject tertiarymark type url versiontype volume


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