dc.creator | Durán López, Lourdes | es |
dc.creator | Domínguez Morales, Juan Pedro | es |
dc.creator | Gutiérrez Galán, Daniel | es |
dc.creator | Ríos Navarro, José Antonio | es |
dc.creator | Jiménez Fernández, Ángel Francisco | es |
dc.creator | Vicente Díaz, Saturnino | es |
dc.creator | Linares Barranco, Alejandro | es |
dc.date.accessioned | 2022-06-29T09:53:19Z | |
dc.date.available | 2022-06-29T09:53:19Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Durán López, L., Domínguez Morales, J.P., Gutiérrez Galán, D., Ríos Navarro, J.A., Jiménez Fernández, Á.F., Vicente Díaz, S. y Linares Barranco, A. (2021). Wide and Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems. Computers in Biology and Medicine, 136 (art. nº 104743) | |
dc.identifier.issn | 0010-4825 | es |
dc.identifier.uri | https://hdl.handle.net/11441/134790 | |
dc.description.abstract | Prostate cancer (PCa) is one of the most commonly diagnosed cancer and one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. Artificial Intelligence algorithms have had a huge impact on medical image analysis, including digital histopathology, where Convolutional Neural Networks (CNNs) are used to provide a fast and accurate diagnosis, supporting experts in this task. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Due to the size of these images, neural networks cannot use them as input and, therefore, small subimages called patches are extracted and predicted, obtaining a patch-level classification. In this work, a novel patch aggregation method based on a custom Wide & Deep neural network model is presented, which performs a slide-level classification using the patch-level classes obtained from a CNN. The malignant tissue ratio, a 10-bin malignant probability histogram, the least squares regression line of the histogram, and the number of malignant connected components are used by the proposed model to perform the classification. An accuracy of 94.24% and a sensitivity of 98.87% were achieved, proving that the proposed system could aid pathologists by speeding up the screening process and, thus, contribute to the fight against PCa. | es |
dc.description.sponsorship | Agencia Estatal de Investigación PID2019-105556 GB-C33/AEI/10.13039/501100011033 | es |
dc.description.sponsorship | European Union H2020 CHIST-ERA SMALL (PCI2019-111841-2) | es |
dc.description.sponsorship | Junta de Andalucía AT17-5410-USE (PROMETEO) | es |
dc.format | application/pdf | es |
dc.format.extent | 10 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Computers in Biology and Medicine, 136 (art. nº 104743) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Prostate cancer | es |
dc.subject | Deep learning | es |
dc.subject | Convolutional neural networks | es |
dc.subject | Computer-aided diagnosis | es |
dc.subject | Patch aggregation | es |
dc.subject | Whole-slide images | es |
dc.subject | Medical image analysis | es |
dc.title | Wide and Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | 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 | PID2019-105556 GB-C33/AEI/10.13039/501100011033 | es |
dc.relation.projectID | CHIST-ERA SMALL (PCI2019-111841-2) | es |
dc.relation.projectID | AT17-5410-USE (PROMETEO) | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0010482521005370?via%3Dihub | es |
dc.identifier.doi | 10.1016/j.compbiomed.2021.104743 | es |
dc.contributor.group | Universidad de Sevilla. TEP-108: Robótica y Tecnología de Computadores | es |
dc.journaltitle | Computers in Biology and Medicine | es |
dc.publication.volumen | 136 | es |
dc.publication.issue | art. nº 104743 | es |
dc.contributor.funder | Agencia Estatal de Investigación. España | es |
dc.contributor.funder | European Union (UE). H2020 | es |
dc.contributor.funder | Junta de Andalucía | es |
dc.description.awardwinning | Premio Mensual Publicación Científica Destacada de la US. Escuela Politécnica Superior | |