A hybrid end-to-end approach integrating conditional random fields into CNNs for prostate cancer detection on MRI

Detalhes bibliográficos
Autor(a) principal: Lapa, Paulo
Data de Publicação: 2020
Outros Autores: Castelli, Mauro, Gonçalves, Ivo, Sala, Evis, Rundo, Leonardo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/92992
Resumo: Lapa, P., Castelli, M., Gonçalves, I., Sala, E., & Rundo, L. (2020). A hybrid end-to-end approach integrating conditional random fields into CNNs for prostate cancer detection on MRI. Applied Sciences (Switzerland), 10(1), [338]. [Special Issue: Deep Learning and Neuro-Evolution Methods in Biomedicine and Bioinformatics)]. Doi: https://doi.org/10.3390/app10010338
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spelling A hybrid end-to-end approach integrating conditional random fields into CNNs for prostate cancer detection on MRIConditional random fieldsConvolutional neural networksMagnetic resonance imagingProstate cancer detectionRecurrent neural networksMaterials Science(all)InstrumentationEngineering(all)Process Chemistry and TechnologyComputer Science ApplicationsFluid Flow and Transfer ProcessesSDG 3 - Good Health and Well-beingLapa, P., Castelli, M., Gonçalves, I., Sala, E., & Rundo, L. (2020). A hybrid end-to-end approach integrating conditional random fields into CNNs for prostate cancer detection on MRI. Applied Sciences (Switzerland), 10(1), [338]. [Special Issue: Deep Learning and Neuro-Evolution Methods in Biomedicine and Bioinformatics)]. Doi: https://doi.org/10.3390/app10010338Prostate Cancer (PCa) is the most common oncological disease inWestern men. Even though a growing effort has been carried out by the scientific community in recent years, accurate and reliable automated PCa detection methods on multiparametric Magnetic Resonance Imaging (mpMRI) are still a compelling issue. In this work, a Deep Neural Network architecture is developed for the task of classifying clinically significant PCa on non-contrast-enhanced MR images. In particular, we propose the use of Conditional Random Fields as a Recurrent Neural Network (CRF-RNN) to enhance the classificationperformance of XmasNet, a Convolutional Neural Network (CNN) architecture specifically tailored to the PROSTATEx17 Challenge. The devised approach builds a hybrid end-to-end trainable network, CRF-XmasNet, composed of an initial CNN component performing feature extraction and a CRF-based probabilistic graphical model component for structured prediction, without the need for two separate training procedures. Experimental results show the suitability of this method in terms ofclassification accuracy and training time, even though the high-variability of the observed results must be reduced before transferring the resulting architecture to a clinical environment. Interestingly, the use of CRFs as a separate postprocessing method achieves significantly lower performance with respect to the proposed hybrid end-to-end approach. The proposed hybrid end-to-end CRF-RNN approach yields excellent peak performance for all the CNN architectures taken into account, but it shows a high-variability, thus requiring future investigation on the integration of CRFs into a CNN.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNLapa, PauloCastelli, MauroGonçalves, IvoSala, EvisRundo, Leonardo2020-02-18T23:33:54Z2020-01-012020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/92992eng2076-3417PURE: 16900903https://doi.org/10.3390/app10010338info: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:RCAAP2024-03-11T04:41:30Zoai:run.unl.pt:10362/92992Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:37:39.343305Repositório 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 A hybrid end-to-end approach integrating conditional random fields into CNNs for prostate cancer detection on MRI
title A hybrid end-to-end approach integrating conditional random fields into CNNs for prostate cancer detection on MRI
spellingShingle A hybrid end-to-end approach integrating conditional random fields into CNNs for prostate cancer detection on MRI
Lapa, Paulo
Conditional random fields
Convolutional neural networks
Magnetic resonance imaging
Prostate cancer detection
Recurrent neural networks
Materials Science(all)
Instrumentation
Engineering(all)
Process Chemistry and Technology
Computer Science Applications
Fluid Flow and Transfer Processes
SDG 3 - Good Health and Well-being
title_short A hybrid end-to-end approach integrating conditional random fields into CNNs for prostate cancer detection on MRI
title_full A hybrid end-to-end approach integrating conditional random fields into CNNs for prostate cancer detection on MRI
title_fullStr A hybrid end-to-end approach integrating conditional random fields into CNNs for prostate cancer detection on MRI
title_full_unstemmed A hybrid end-to-end approach integrating conditional random fields into CNNs for prostate cancer detection on MRI
title_sort A hybrid end-to-end approach integrating conditional random fields into CNNs for prostate cancer detection on MRI
author Lapa, Paulo
author_facet Lapa, Paulo
Castelli, Mauro
Gonçalves, Ivo
Sala, Evis
Rundo, Leonardo
author_role author
author2 Castelli, Mauro
Gonçalves, Ivo
Sala, Evis
Rundo, Leonardo
author2_role author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Lapa, Paulo
Castelli, Mauro
Gonçalves, Ivo
Sala, Evis
Rundo, Leonardo
dc.subject.por.fl_str_mv Conditional random fields
Convolutional neural networks
Magnetic resonance imaging
Prostate cancer detection
Recurrent neural networks
Materials Science(all)
Instrumentation
Engineering(all)
Process Chemistry and Technology
Computer Science Applications
Fluid Flow and Transfer Processes
SDG 3 - Good Health and Well-being
topic Conditional random fields
Convolutional neural networks
Magnetic resonance imaging
Prostate cancer detection
Recurrent neural networks
Materials Science(all)
Instrumentation
Engineering(all)
Process Chemistry and Technology
Computer Science Applications
Fluid Flow and Transfer Processes
SDG 3 - Good Health and Well-being
description Lapa, P., Castelli, M., Gonçalves, I., Sala, E., & Rundo, L. (2020). A hybrid end-to-end approach integrating conditional random fields into CNNs for prostate cancer detection on MRI. Applied Sciences (Switzerland), 10(1), [338]. [Special Issue: Deep Learning and Neuro-Evolution Methods in Biomedicine and Bioinformatics)]. Doi: https://doi.org/10.3390/app10010338
publishDate 2020
dc.date.none.fl_str_mv 2020-02-18T23:33:54Z
2020-01-01
2020-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/92992
url http://hdl.handle.net/10362/92992
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2076-3417
PURE: 16900903
https://doi.org/10.3390/app10010338
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
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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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