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Deep-Learning-based Classification of Rat OCT images After Intravitreal Injection of ET-1 for Glaucoma Understanding

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Deep-Learning-based Classification of Rat OCT images After Intravitreal Injection of ET-1 for Glaucoma Understanding

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dc.contributor.author Fuentes-Hurtado, Félix José es_ES
dc.contributor.author Morales, Sandra es_ES
dc.contributor.author Mossi García, José Manuel es_ES
dc.contributor.author Naranjo Ornedo, Valeriana es_ES
dc.contributor.author Fedulov, Vadim es_ES
dc.contributor.author Woldbye, David es_ES
dc.contributor.author Klemp, Kristian es_ES
dc.contributor.author Torm, Marie es_ES
dc.contributor.author Larsen, Michael es_ES
dc.date.accessioned 2019-07-25T09:23:44Z
dc.date.available 2019-07-25T09:23:44Z
dc.date.issued 2018-11-09
dc.identifier.isbn 978-3-030-03492-4
dc.identifier.uri http://hdl.handle.net/10251/124182
dc.description.abstract Optical coherence tomography (OCT) is a useful technique to monitor retinal damage. We present an automatic method to accurately classify rodent OCT images in healthy and pathological (before and after 14 days of intravitreal injection of Endothelin-1, respectively) making use of the DenseNet-201 architecture fine-tuned and a customized top-model. We validated the performance of the method on 1912 OCT images yielding promising results ( AUC=0.99±0.01 in a P=15 leave-P-out cross-validation). Besides, we also compared the results of the fine-tuned network with those achieved training the network from scratch, obtaining some interesting insights. The presented method poses a step forward in understanding pathological rodent OCT retinal images, as at the moment there is no known discriminating characteristic which allows classifying this type of images accurately. The result of this work is a very accurate and robust automatic method to distinguish between healthy and a rodent model of glaucoma, which is the backbone of future works dealing with human OCT images. es_ES
dc.description.sponsorship Animal experiment permission was granted by the Danish Animal Experimentation Council (license number: 2017-15-0201-01213). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. This work was supported by the Project GALAHAD [H2020-ICT-2016-2017, 732613] es_ES
dc.format.extent 8 es_ES
dc.language Inglés es_ES
dc.publisher Springer es_ES
dc.relation.ispartof Intelligent Data Engineering and Automated Learning – IDEAL 2018 es_ES
dc.relation.ispartofseries Lecture Notes in Computer Science;11314
dc.rights Reserva de todos los derechos es_ES
dc.subject Optical coherence tomography es_ES
dc.subject Deep-learning es_ES
dc.subject Glaucoma es_ES
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.title Deep-Learning-based Classification of Rat OCT images After Intravitreal Injection of ET-1 for Glaucoma Understanding es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.1007/978-3-030-03493-1_4
dc.relation.projectID info:eu-repo/grantAgreement/Animal Experimentation Council, Dinamarca//2017-15-0201-01213/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/732613/EU/Glaucoma – Advanced, LAbel-free High resolution Automated OCT Diagnostics/ 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 Fuentes-Hurtado, FJ.; Morales, S.; Mossi García, JM.; Naranjo Ornedo, V.; Fedulov, V.; Woldbye, D.; Klemp, K.... (2018). Deep-Learning-based Classification of Rat OCT images After Intravitreal Injection of ET-1 for Glaucoma Understanding. En Intelligent Data Engineering and Automated Learning – IDEAL 2018. Springer. 27-34. https://doi.org/10.1007/978-3-030-03493-1_4 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) es_ES
dc.relation.conferencedate Noviembre 21-23,2018 es_ES
dc.relation.conferenceplace Madrid, Spain es_ES
dc.relation.publisherversion https://doi.org/10.1007/978-3-030-03493-1_4 es_ES
dc.description.upvformatpinicio 27 es_ES
dc.description.upvformatpfin 34 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela S\376337 es_ES
dc.contributor.funder Animal Experimentation Council, Dinamarca es_ES
dc.contributor.funder European Commission es_ES
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