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Deep Learning for Skin Melanocytic Tumors in Whole-Slide Images: A Systematic Review

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Deep Learning for Skin Melanocytic Tumors in Whole-Slide Images: A Systematic Review

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Mosquera-Zamudio, A.; Launet, L.; Tabatabaei, Z.; Parra-Medina, R.; Colomer, A.; Oliver Moll, J.; Monteagudo, C.... (2023). Deep Learning for Skin Melanocytic Tumors in Whole-Slide Images: A Systematic Review. Cancers. 15(1):1-19. https://doi.org/10.3390/cancers15010042

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/192533

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Title: Deep Learning for Skin Melanocytic Tumors in Whole-Slide Images: A Systematic Review
Author: Mosquera-Zamudio, Andrés Launet, Laetitia Tabatabaei, Zahra Parra-Medina, Rafael Colomer, Adrián Oliver Moll, Javier Monteagudo, Carlos Janssen, Emiel Naranjo Ornedo, Valeriana
UPV Unit: Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Telecomunicación - Escola Tècnica Superior d'Enginyers de Telecomunicació
Issued date:
Abstract:
[EN] Simple Summary Deep learning (DL) is expanding into the surgical pathology field and shows promising outcomes in diminishing subjective interpretations, especially in dermatopathology. We aim to show the efforts of ...[+]
Subjects: Skin , Cancer , Melanoma , Melanocytic tumors , Dermatopathology , Computational pathology , Deep learning , Classification , Segmentation , Computer-aided diagnosis
Copyrigths: Reconocimiento (by)
Source:
Cancers. (eissn: 2072-6694 )
DOI: 10.3390/cancers15010042
Publisher:
MDPI AG
Publisher version: https://doi.org/10.3390/cancers15010042
Project ID:
info:eu-repo/grantAgreement/EC/H2020/860627/EU
info:eu-repo/grantAgreement/UPV//PAID-10-21/
info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 (ISCIII)/PI20%2F00094/ES/ANALISIS COMBINADO POR INTELIGENCIA ARTIFICIAL DE MARCADORES EPIGENETICOS E IMAGENES MICROSCOPICAS DIGITALIZADAS DE TUMORES MELANOCITICOS AMBIGUOS PARA OPTIMIZAR SU CLASIFICACION DIAGNOSTICA Y PRONOSTICA/
info:eu-repo/grantAgreement/UPV//PAID-PD-22/
info:eu-repo/grantAgreement/GVA//INNEST%2F2021%2F321/
Thanks:
This work has received funding from the European Union's Horizon 2020 Programme for Research and Innovation, under the Marie Sklodowska Curie grant agreement No. 860627 (CLARIFY). The work is also supported by project ...[+]
Type: Artículo

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