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dc.contributor.advisorBeltrán Castañón, César Armando
dc.contributor.authorGarcía Ríos, Emilio Rafaeles_ES
dc.date.accessioned2018-01-19T21:32:22Zes_ES
dc.date.available2018-01-19T21:32:22Zes_ES
dc.date.created2017es_ES
dc.date.issued2018-01-19es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12404/9905
dc.description.abstractTumor-infiltrating lymphocytes (TILs) have received considerable attention in recent years, as evidence suggests they are related to cancer prognosis. Distribution and localization of these and other types of immune cells are of special interest for pathologists, and frequently involve manual examination on Immunohistochemistry (IHC) Images. We present a model based on Deep Convolutional Neural Networks for Automatic lymphocyte detection on IHC images of gastric cancer. The dataset created as part of this work is publicly available for future research.es_ES
dc.description.uriTesises_ES
dc.language.isoenges_ES
dc.publisherPontificia Universidad Católica del Perúes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/pe/*
dc.subjectCánceres_ES
dc.subjectGastroenterologíaes_ES
dc.subjectAprendizaje automático (Inteligencia artificial)es_ES
dc.titleAutomatic lymphocyte detection on gastric cancer IHC images using deep learninges_ES
dc.typeinfo:eu-repo/semantics/masterThesises_ES
thesis.degree.nameMaestro en Informática con mención en Ciencias de la Computaciónes_ES
thesis.degree.levelMaestríaes_ES
thesis.degree.grantorPontificia Universidad Católica del Perú. Escuela de Posgradoes_ES
thesis.degree.disciplineInformática con mención en Ciencias de la Computaciónes_ES
renati.advisor.dni29561260
renati.advisor.orcidhttps://orcid.org/0000-0002-0173-4140es_ES
renati.discipline611087es_ES
renati.levelhttps://purl.org/pe-repo/renati/level#maestroes_ES
renati.typehttp://purl.org/pe-repo/renati/type#tesises_ES
dc.publisher.countryPEes_ES
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.02.00es_ES


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