Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/105772
DC FieldValueLanguage
dc.contributor.authorLapa, Paulo-
dc.contributor.authorCastelli, Mauro-
dc.contributor.authorGonçalves, Ivo-
dc.contributor.authorSala, Evis-
dc.contributor.authorRundo, Leonardo-
dc.date.accessioned2023-03-07T12:36:03Z-
dc.date.available2023-03-07T12:36:03Z-
dc.date.issued2020-
dc.identifier.issn2076-3417pt
dc.identifier.urihttps://hdl.handle.net/10316/105772-
dc.description.abstractProstate 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 classification performance 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 of classification 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.pt
dc.language.isoengpt
dc.publisherMDPIpt
dc.relationUID/MULTI/00308/2019pt
dc.relationPOCI-01-0145-FEDER-028040pt
dc.relationDSAIPA/DS/0022/2018 (GADgET)pt
dc.relationSlovenian Research Agency (research core funding No. P5-0410)pt
dc.relationThe Mark Foundation for Cancer Research and Cancer Research UK Cambridge Centre [C9685/A25177]pt
dc.relationNational Institute of Health Research (NIHR) Cambridge Biomedical Research Centrept
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectprostate cancer detectionpt
dc.subjectmagnetic resonance imagingpt
dc.subjectconvolutional neural networkspt
dc.subjectconditional random fieldspt
dc.subjectrecurrent neural networkspt
dc.titleA Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRIpt
dc.typearticle-
degois.publication.firstPage338pt
degois.publication.issue1pt
degois.publication.titleApplied Sciences (Switzerland)pt
dc.peerreviewedyespt
dc.identifier.doi10.3390/app10010338pt
degois.publication.volume10pt
dc.date.embargo2020-01-01*
uc.date.periodoEmbargo0pt
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypearticle-
item.fulltextCom Texto completo-
item.grantfulltextopen-
item.cerifentitytypePublications-
Appears in Collections:I&D INESCC - Artigos em Revistas Internacionais
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