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dc.contributor.authorLapa, Pen
dc.contributor.authorCastelli, Men
dc.contributor.authorGonçalves, Ien
dc.contributor.authorSala, Evisen
dc.contributor.authorRundo, Leonardoen
dc.date.accessioned2020-09-17T23:31:01Z
dc.date.available2020-09-17T23:31:01Z
dc.date.issued2020-01-01en
dc.identifier.issn2076-3417
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/310433
dc.description.abstract© 2020 by the authors. Prostate 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.
dc.publisherMDPI AG
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleA hybrid end-to-end approach integrating conditional random fields into CNNs for prostate cancer detection on MRIen
dc.typeArticle
prism.issueIdentifier1en
prism.publicationDate2020en
prism.publicationNameApplied Sciences (Switzerland)en
prism.volume10en
dc.identifier.doi10.17863/CAM.57527
dcterms.dateAccepted2019-12-24en
rioxxterms.versionofrecord10.3390/app10010338en
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2020-01-01en
dc.contributor.orcidCastelli, M [0000-0002-8793-1451]
dc.contributor.orcidGonçalves, I [0000-0002-5336-7768]
dc.contributor.orcidSala, Evis [0000-0002-5518-9360]
dc.contributor.orcidRundo, Leonardo [0000-0003-3341-5483]
dc.identifier.eissn2076-3417
rioxxterms.typeJournal Article/Reviewen


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International