1. Repositório Aberto da Universidade do Porto
  2. FEUP - Faculdade de Engenharia
  3. FEUP - Dissertação
Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/169698
Full metadata record
DC FieldValueLanguage
dc.creatorAfonso José Pinheiro Oliveira Esteves Abreu
dc.date.accessioned2026-01-26T01:29:44Z-
dc.date.available2026-01-26T01:29:44Z-
dc.date.issued2025-09-19
dc.date.submitted2025-10-01
dc.identifier.othersigarra:743674
dc.identifier.urihttps://hdl.handle.net/10216/169698-
dc.descriptionThe development of computer-aided systems for cancer characterisation has been an intense research field, considering the value of the outcomes for more adequate and personalized treatment plan assignments to increase survival chances. Medical images have been shown to provide valuable information on the biological phenomena associated with cancer development, which can be used to design Artificial Intelligence (AI)-based predictive models for support in the clinical routine. From the clinical side, the imaging annotation process can be extremely difficult, subjective and time-consuming, which motivates the development of models that can benefit from labelled and non-labeled data.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectEngenharia electrotécnica, electrónica e informática
dc.subjectElectrical engineering, Electronic engineering, Information engineering
dc.titleOvercoming scarce annotations through deep semi-supervised learning in cancer characterisation
dc.typeDissertação
dc.contributor.uportoFaculdade de Engenharia
dc.identifier.tid204109906
dc.subject.fosCiências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática
dc.subject.fosEngineering and technology::Electrical engineering, Electronic engineering, Information engineering
thesis.degree.disciplineMestrado em Engenharia Informática e Computação
thesis.degree.grantorFaculdade de Engenharia
thesis.degree.grantorUniversidade do Porto
thesis.degree.level1
Appears in Collections:FEUP - Dissertação

Files in This Item:
File Description SizeFormat 
743674.pdfOvercoming scarce annotations through deep semi-supervised learning in cancer characterisation1.99 MBAdobe PDFThumbnail
View/Open
Show simple item record Recommend this item Display Statistics


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.