dc.creator | Durán López, Lourdes | es |
dc.creator | Domínguez Morales, Juan Pedro | es |
dc.creator | Conde Martín, A.F. | es |
dc.creator | Vicente Díaz, Saturnino | es |
dc.creator | Linares Barranco, Alejandro | es |
dc.date.accessioned | 2021-04-12T08:53:53Z | |
dc.date.available | 2021-04-12T08:53:53Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Durán 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.issn | 2169-3536 | es |
dc.identifier.uri | https://hdl.handle.net/11441/106950 | |
dc.description.abstract | Prostate 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.sponsorship | Ministerio de Economía y Competitividad COFNET TEC2016-77785-P | es |
dc.description.sponsorship | Junta de Andalucía AT17_5410_USE | es |
dc.format | application/pdf | es |
dc.format.extent | 16 | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | IEEE Access, 8, 128613-128628. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Convolutional Neural Networks (CNN) | es |
dc.subject | Computer-aided diagnosis | es |
dc.subject | Deep learning | es |
dc.subject | Medical Image Analysis | es |
dc.subject | Prostate cancer | es |
dc.subject | Whole-slide images | es |
dc.title | PROMETEO: A CNN-based computer-aided diagnosis system for WSI prostate cancer detection | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores | es |
dc.relation.projectID | COFNET TEC2016-77785-P | es |
dc.relation.projectID | AT17_5410_USE | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9139241 | es |
dc.identifier.doi | 10.1109/ACCESS.2020.3008868 | es |
dc.contributor.group | Universidad de Sevilla. TEP-108: Robótica y Tecnología de Computadores Aplicada a la Rehabilitación | es |
dc.journaltitle | IEEE Access | es |
dc.publication.volumen | 8 | es |
dc.publication.initialPage | 128613 | es |
dc.publication.endPage | 128628 | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |
dc.contributor.funder | Junta de Andalucía | es |