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dc.contributor.author | Golfe-San Martín, Alejandro | es_ES |
dc.contributor.author | del Amor, Rocío | es_ES |
dc.contributor.author | Colomer, Adrián | es_ES |
dc.contributor.author | Sales, María A. | es_ES |
dc.contributor.author | Terradez, Liria | es_ES |
dc.contributor.author | Naranjo Ornedo, Valeriana | es_ES |
dc.date.accessioned | 2024-06-12T18:18:40Z | |
dc.date.available | 2024-06-12T18:18:40Z | |
dc.date.issued | 2023-10 | es_ES |
dc.identifier.issn | 0169-2607 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/205076 | |
dc.description.abstract | [EN] Background and objective: Prostate cancer is one of the most common diseases affecting men. The main diagnostic and prognostic reference tool is the Gleason scoring system. An expert pathologist assigns a Gleason grade to a sample of prostate tissue. As this process is very time-consuming, some artificial intelligence applications were developed to automatize it. The training process is often confronted with insufficient and unbalanced databases which affect the generalisability of the models. Therefore, the aim of this work is to develop a generative deep learning model capable of synthesising patches of any selected Gleason grade to perform data augmentation on unbalanced data and test the improvement of classification models.Methodology: The methodology proposed in this work consists of a conditional Progressive Growing GAN (ProGleason-GAN) capable of synthesising prostate histopathological tissue patches by selecting the desired Gleason Grade cancer pattern in the synthetic sample. The conditional Gleason Grade information is introduced into the model through the embedding layers, so there is no need to add a term to the Wasserstein loss function. We used minibatch standard deviation and pixel normalisation to improve the performance and stability of the training process.Results: The reality assessment of the synthetic samples was performed with the Frechet Inception Distance (FID). We obtained an FID metric of 88.85 for non-cancerous patterns, 81.86 for GG3, 49.32 for GG4 and 108.69 for GG5 after post-processing stain normalisation. In addition, a group of expert pathologists was selected to perform an external validation of the proposed framework. Finally, the application of our proposed framework improved the classification results in SICAPv2 dataset, proving its effectiveness as a data augmentation method.Conclusions: ProGleason-GAN approach combined with a stain normalisation post-processing provides state-of-the-art results regarding Frechet's Inception Distance. This model can synthesise samples of noncancerous patterns, GG3, GG4 or GG5. The inclusion of conditional information about the Gleason grade during the training process allows the model to select the cancerous pattern in a synthetic sample. The proposed framework can be used as a data augmentation method. | es_ES |
dc.description.sponsorship | This work has received funding & nbsp;from Horizon 2020 , the European Union's Framework Programme for Research and Innovation, under grant agreement no. 860627 (CLARIFY), the Spanish Ministry of Economy and Competitiveness through project PID2019-105142RB-C21 (AI4SKIN) and GVA through projects PROME-TEO/2019/109 and INNEST/2021/321 (SAMUEL). The work of Adrian Colomer has been supported by the ValgrAI - Valencian Graduate School and Research Network for Artificial Intelligence & Generalitat Valenciana and Universitat Politecnica de Valencia (PAID-PD-22). Rocio del Amor has been supported by the Spanish Government under FPU Grant (FPU20/05263). | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Computer Methods and Programs in Biomedicine | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Prostate cancer | es_ES |
dc.subject | Progressive growing GAN | es_ES |
dc.subject | Conditional GAN | es_ES |
dc.subject | Gleason grade | es_ES |
dc.subject.classification | ESTADISTICA E INVESTIGACION OPERATIVA | es_ES |
dc.subject.classification | TEORÍA DE LA SEÑAL Y COMUNICACIONES | es_ES |
dc.title | ProGleason-GAN: Conditional Progressive Growing GAN for prostatic cancer Gleason Grade patch synthesis | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.cmpb.2023.107695 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-105142RB-C21/ES/CARACTERIZACION DE NEOPLASIAS DE CELULAS FUSIFORMES EN IMAGENES HISTOLOGICAS/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/860627/EU/CLoud ARtificial Intelligence For pathologY/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/Instituto de Salud Carlos III//PI20%2F00094//Análisis combinado por Inteligencia Artificial de marcadores epigenéticos e imágenes microscópicas digitalizadas de tumores melanocíticos ambiguos para optimizar su clasificación diagnóstica y pronóstica/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//ACIF%2F2021%2F124//SISTEMA AUTOMATICO DE CLASIFICACION DE NEOPLASIAS CUTANEAS DE CELULAS FUSIFORMES BASADO EN INTELIGENCIA ARTIFICIAL/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/UPV//PAID-PD-22/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//PROMETEO%2F2019%2F109//COMUNICACION Y COMPUTACION INTELIGENTES Y SOCIALES/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//INNEST%2F2021%2F321/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MIU//FPU20%2F05263/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Telecomunicación - Escola Tècnica Superior d'Enginyers de Telecomunicació | es_ES |
dc.description.bibliographicCitation | Golfe-San Martín, A.; Del Amor, R.; Colomer, A.; Sales, MA.; Terradez, L.; Naranjo Ornedo, V. (2023). ProGleason-GAN: Conditional Progressive Growing GAN for prostatic cancer Gleason Grade patch synthesis. Computer Methods and Programs in Biomedicine. 240. https://doi.org/10.1016/j.cmpb.2023.107695 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.cmpb.2023.107695 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 240 | es_ES |
dc.identifier.pmid | 37393742 | es_ES |
dc.relation.pasarela | S\495910 | es_ES |
dc.contributor.funder | GENERALITAT VALENCIANA | es_ES |
dc.contributor.funder | Ministerio de Universidades | es_ES |
dc.contributor.funder | Instituto de Salud Carlos III | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | COMISION DE LAS COMUNIDADES EUROPEA | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |
dc.contributor.funder | Valencian Graduate School and Research Network of Artificial Intelligence | es_ES |