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dc.creatorDurán López, Lourdeses
dc.creatorDomínguez Morales, Juan Pedroes
dc.creatorConde Martín, A.F.es
dc.creatorVicente Díaz, Saturninoes
dc.creatorLinares Barranco, Alejandroes
dc.date.accessioned2021-04-12T08:53:53Z
dc.date.available2021-04-12T08:53:53Z
dc.date.issued2020
dc.identifier.citationDurán López, L., Domínguez Morales, J.P., Conde Martín, A.F., Vicente Díaz, S. y Linares Barranco, A. (2020). PROMETEO: A CNN-based computer-aided diagnosis system for WSI prostate cancer detection. IEEE Access, 8, 128613-128628.
dc.identifier.issn2169-3536es
dc.identifier.urihttps://hdl.handle.net/11441/106950
dc.description.abstractProstate cancer is currently one of the most commonly-diagnosed types of cancer among males. Although its death rate has dropped in the last decades, it is still a major concern and one of the leading causes of cancer death. Prostate biopsy is a test that confirms or excludes the presence of cancer in the tissue. Samples extracted from biopsies are processed and digitized, obtaining gigapixel-resolution images called wholeslide images, which are analyzed by pathologists. Automated intelligent systems could be useful for helping pathologists in this analysis, reducing fatigue and making the routine process faster. In this work, a novel Deep Learning based computer-aided diagnosis system is presented. This system is able to analyze wholeslide histology images that are first patch-sampled and preprocessed using different filters, including a novel patch-scoring algorithm that removes worthless areas from the tissue. Then, patches are used as input to a custom Convolutional Neural Network, which gives a report showing malignant regions on a heatmap. The impact of applying a stain-normalization process to the patches is also analyzed in order to reduce color variability between different scanners. After training the network with a 3-fold cross-validation method, 99.98% accuracy, 99.98% F1 score and 0.999 AUC are achieved on a separate test set. The computation time needed to obtain the heatmap of a whole-slide image is, on average, around 15 s. Our custom network outperforms other state-of-the-art works in terms of computational complexity for a binary classification task between normal and malignant prostate whole-slide images at patch level.es
dc.description.sponsorshipMinisterio de Economía y Competitividad COFNET TEC2016-77785-Pes
dc.description.sponsorshipJunta de Andalucía AT17_5410_USEes
dc.formatapplication/pdfes
dc.format.extent16es
dc.language.isoenges
dc.publisherIEEE Computer Societyes
dc.relation.ispartofIEEE Access, 8, 128613-128628.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectConvolutional Neural Networks (CNN)es
dc.subjectComputer-aided diagnosises
dc.subjectDeep learninges
dc.subjectMedical Image Analysises
dc.subjectProstate canceres
dc.subjectWhole-slide imageses
dc.titlePROMETEO: A CNN-based computer-aided diagnosis system for WSI prostate cancer detectiones
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadoreses
dc.relation.projectIDCOFNET TEC2016-77785-Pes
dc.relation.projectIDAT17_5410_USEes
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9139241es
dc.identifier.doi10.1109/ACCESS.2020.3008868es
dc.contributor.groupUniversidad de Sevilla. TEP-108: Robótica y Tecnología de Computadores Aplicada a la Rehabilitaciónes
dc.journaltitleIEEE Accesses
dc.publication.volumen8es
dc.publication.initialPage128613es
dc.publication.endPage128628es
dc.contributor.funderMinisterio de Economía y Competitividad (MINECO). Españaes
dc.contributor.funderJunta de Andalucíaes

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