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dc.contributor.author | del Amor, Rocío | es_ES |
dc.contributor.author | Colomer, Adrián | es_ES |
dc.contributor.author | Monteagudo, Carlos | es_ES |
dc.contributor.author | Garzón, María José | es_ES |
dc.contributor.author | García-Giménez, José Luis | es_ES |
dc.contributor.author | Naranjo Ornedo, Valeriana | es_ES |
dc.date.accessioned | 2022-01-20T07:32:23Z | |
dc.date.available | 2022-01-20T07:32:23Z | |
dc.date.issued | 2021-08-27 | es_ES |
dc.identifier.isbn | 978-9-0827-9706-0 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/179966 | |
dc.description.abstract | [EN] Spitzoid melanocytic tumors (SMT) are a group of neoplasms that represent a formidable diagnostic challenge for dermatopathologists. DNA methylation (DNAm) is a welldefined epigenetic factor that has an important role in the development of these lesions. In this work, we propose different deep-learning-based approaches to address the Spitzoid neoplasms detection from DNAm. We use an autoencoder and a variational autoencoder for dimensionality reduction with a subsequently supervised classification. Additionally, we present a deep embedded refined clustering algorithm able to optimize the latent space at the same time that the non-supervised classification task is performed. This novel approach in DNAm supposes a step forward in the SMT detection as suggest the obtained results (acc = 0.9). Additionally, making use of the resulting model, we present a subspace-prototypical-based approach for the prognostic prediction of uncertain malignant potential samples, which is nowadays the hottest open area in SMT detection. | es_ES |
dc.description.sponsorship | This work has received funding 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 SICAP (DPI2016-77869-C2-1-R) and GVA through project PROMETEO/2019/109 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | IEEE | es_ES |
dc.relation.ispartof | 2021 29th European Signal Processing Conference (EUSIPCO) | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Dimensionality reduction | es_ES |
dc.subject | Deep embedded refined clustering, DNA methylation | es_ES |
dc.subject | Spitzoid neoplasms | es_ES |
dc.subject.classification | TEORIA DE LA SEÑAL Y COMUNICACIONES | es_ES |
dc.title | A Deep Embedded Framework for Spitzoid Neoplasm Classification Using DNA Methylation Data | es_ES |
dc.type | Comunicación en congreso | es_ES |
dc.type | Capítulo de libro | es_ES |
dc.identifier.doi | 10.23919/EUSIPCO54536.2021.9616137 | 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///DPI2016-77869-C2-1-R//SISTEMA DE INTERPRETACION DE IMAGENES HISTOPATOLOGICAS PARA LA DETECCION DE CANCER DE PROSTATA/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/860627/EU/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement///PROMETEO%2F2019%2F109//COMUNICACION Y COMPUTACION INTELIGENTES Y SOCIALES/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions | es_ES |
dc.description.bibliographicCitation | Del Amor, R.; Colomer, A.; Monteagudo, C.; Garzón, MJ.; García-Giménez, JL.; Naranjo Ornedo, V. (2021). A Deep Embedded Framework for Spitzoid Neoplasm Classification Using DNA Methylation Data. IEEE. 1271-1275. https://doi.org/10.23919/EUSIPCO54536.2021.9616137 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.conferencename | 29th European Signal Processing Conference (EUSIPCO 2021) | es_ES |
dc.relation.conferencedate | Agosto 23-27,2021 | es_ES |
dc.relation.conferenceplace | Online | es_ES |
dc.relation.publisherversion | https://doi.org/10.23919/EUSIPCO54536.2021.9616137 | es_ES |
dc.description.upvformatpinicio | 1271 | es_ES |
dc.description.upvformatpfin | 1275 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.relation.pasarela | S\451590 | es_ES |
dc.contributor.funder | European Commission | es_ES |