Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/71341

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dc.contributor.authorDias, Andrépor
dc.contributor.authorFernandes, João Vieirapor
dc.contributor.authorMonteiro, Ruipor
dc.contributor.authorMachado, Joanapor
dc.contributor.authorFerraz, Filipa Tinocopor
dc.contributor.authorNeves, Joãopor
dc.contributor.authorSampaio, Luziapor
dc.contributor.authorRibeiro, Jorgepor
dc.contributor.authorVicente, Henriquepor
dc.contributor.authorAlves, Victorpor
dc.contributor.authorNeves, Josépor
dc.date.accessioned2021-04-06T19:50:48Z-
dc.date.issued2019-
dc.identifier.citationDias A. et al. (2019) A Deep Learning Line to Assess Patient’s Lung Cancer Stages. In: Yang XS., Sherratt S., Dey N., Joshi A. (eds) Third International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 797. Springer, Singapore. https://doi.org/10.1007/978-981-13-1165-9_55por
dc.identifier.isbn978-981-13-1164-2-
dc.identifier.issn2194-5357-
dc.identifier.urihttps://hdl.handle.net/1822/71341-
dc.description.abstractOur goal is to pursue a vision of developing and maintaining a comprehensive and integrated computer model to help physicians plan the most appropriate treatment and anticipate a patient’s prospects for the extent of cancer. For example, cancer can be treated at an early stage by surgery or radiation, while chemotherapy may be the care for more advanced stages. In fact, early detection of this type of cancer facilitates its treatment and may rise the patients’ prospect of a continued existence. Thus, a formal view of an intelligent system for performing cancer feature extraction and analysis in order to establish the bases that will help physicians plan treatment and predict patient’s prognosis is presented. It is based on the Logic Programming Language and draws a line between Deep Learning and Knowledge Representation and Reasoning, and is supported by a Case Based attitude to computing. In fact, despite the fact that each patient’s condition is different, treating cancer at the same stage is often similar.por
dc.description.sponsorshipThis work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.por
dc.language.isoengpor
dc.publisherSpringer Verlagpor
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147280/PTpor
dc.rightsrestrictedAccesspor
dc.subjectCase-based reasoningpor
dc.subjectComputed Tomographypor
dc.subjectIntelligent systemspor
dc.subjectKnowledge representation and reasoningpor
dc.subjectLogic programmingpor
dc.subjectLung cancerpor
dc.titleA deep learning line to assess patient’s lung cancer stagespor
dc.typeconferencePaperpor
dc.peerreviewedyespor
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007%2F978-981-13-1165-9_55por
oaire.citationStartPage599por
oaire.citationEndPage607por
oaire.citationVolume797por
dc.date.updated2021-04-06T17:16:56Z-
dc.identifier.doi10.1007/978-981-13-1165-9_55por
dc.date.embargo10000-01-01-
dc.identifier.eisbn978-981-13-1165-9-
dc.subject.fosCiências Naturais::Ciências da Computação e da Informaçãopor
dc.subject.wosScience & Technology-
sdum.export.identifier10235-
sdum.journalAdvances in Intelligent Systems and Computingpor
sdum.conferencePublicationTHIRD INTERNATIONAL CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGYpor
Aparece nas coleções:CAlg - Artigos em livros de atas/Papers in proceedings

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