Autoencoder-based Image Recommendation for Lung Cancer Characterization

Detalhes bibliográficos
Autor(a) principal: Guilherme Carlos Salles
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/152144
Resumo: In this project, we aim to develop an AI system that recommends a set of relative (past) cases to guide the decision-making of the clinician. Objective: The ambition is to develop an AI-based learning model for lung cancer characterization in order to assist in clinical routine. Considering the complexity of the biological phenomenat hat occur during cancer development, relationships between these and visual manifestations captured by CT have been explored in recent years; however, given the lack of robustness of current deep learning methods, these correlations are often found spurious and get lost when facing data collected from shifted distributions: different institutions, demographics or even stages of cancer development.
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spelling Autoencoder-based Image Recommendation for Lung Cancer CharacterizationOutras ciências da engenharia e tecnologiasOther engineering and technologiesIn this project, we aim to develop an AI system that recommends a set of relative (past) cases to guide the decision-making of the clinician. Objective: The ambition is to develop an AI-based learning model for lung cancer characterization in order to assist in clinical routine. Considering the complexity of the biological phenomenat hat occur during cancer development, relationships between these and visual manifestations captured by CT have been explored in recent years; however, given the lack of robustness of current deep learning methods, these correlations are often found spurious and get lost when facing data collected from shifted distributions: different institutions, demographics or even stages of cancer development.2023-07-122023-07-12T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/152144TID:203423798engGuilherme Carlos Sallesinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-22T01:31:36Zoai:repositorio-aberto.up.pt:10216/152144Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:05:14.849989Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Autoencoder-based Image Recommendation for Lung Cancer Characterization
title Autoencoder-based Image Recommendation for Lung Cancer Characterization
spellingShingle Autoencoder-based Image Recommendation for Lung Cancer Characterization
Guilherme Carlos Salles
Outras ciências da engenharia e tecnologias
Other engineering and technologies
title_short Autoencoder-based Image Recommendation for Lung Cancer Characterization
title_full Autoencoder-based Image Recommendation for Lung Cancer Characterization
title_fullStr Autoencoder-based Image Recommendation for Lung Cancer Characterization
title_full_unstemmed Autoencoder-based Image Recommendation for Lung Cancer Characterization
title_sort Autoencoder-based Image Recommendation for Lung Cancer Characterization
author Guilherme Carlos Salles
author_facet Guilherme Carlos Salles
author_role author
dc.contributor.author.fl_str_mv Guilherme Carlos Salles
dc.subject.por.fl_str_mv Outras ciências da engenharia e tecnologias
Other engineering and technologies
topic Outras ciências da engenharia e tecnologias
Other engineering and technologies
description In this project, we aim to develop an AI system that recommends a set of relative (past) cases to guide the decision-making of the clinician. Objective: The ambition is to develop an AI-based learning model for lung cancer characterization in order to assist in clinical routine. Considering the complexity of the biological phenomenat hat occur during cancer development, relationships between these and visual manifestations captured by CT have been explored in recent years; however, given the lack of robustness of current deep learning methods, these correlations are often found spurious and get lost when facing data collected from shifted distributions: different institutions, demographics or even stages of cancer development.
publishDate 2023
dc.date.none.fl_str_mv 2023-07-12
2023-07-12T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/10216/152144
TID:203423798
url https://hdl.handle.net/10216/152144
identifier_str_mv TID:203423798
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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