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dc.contributor.authorGórriz, Marc
dc.contributor.authorAntony, Joseph
dc.contributor.authorMcGuinness, Kevin
dc.contributor.authorGiró Nieto, Xavier
dc.contributor.authorO'Connor, Noel
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2019-07-18T08:04:59Z
dc.date.available2019-07-18T08:04:59Z
dc.date.issued2019
dc.identifier.citationGórriz, M. [et al.]. Assessing knee OA severity with CNN attention-based end-to-end architectures. A: International conference on Medical Imaging with Deep Learning. "International Conference on Medical Imaging with Deep Learning: 8-10 July 2019, London, United Kingdom: proceedings of Machine Learning Research". 2019, p. 197-214.
dc.identifier.isbn2640-3498
dc.identifier.otherhttps://openreview.net/forum?id=B1epyN8rlV
dc.identifier.urihttp://hdl.handle.net/2117/166395
dc.description.abstractThis work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing state-of-the-art CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST).
dc.format.extent18 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
dc.subjectÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
dc.subjectÀrees temàtiques de la UPC::Ciències de la salut::Medicina::Diagnòstic per la imatge
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshEnd-to-end delay (Computer networks)
dc.subject.lcshComputer algorithms
dc.subject.otherMedical imaging
dc.subject.otherKnee osteoarthritis
dc.subject.otherConbolutional neural networks
dc.subject.otherDeep learning
dc.subject.otherComputer vision
dc.titleAssessing knee OA severity with CNN attention-based end-to-end architectures
dc.typeConference lecture
dc.subject.lemacXarxes neuronals (Informàtica)
dc.subject.lemacAlgorismes computacionals
dc.subject.lemacAprenentatge automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.relation.publisherversionhttp://proceedings.mlr.press/v102/gorriz19a.html
dc.rights.accessOpen Access
local.identifier.drac25501480
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/2PE/ TEC2016-75976-R
local.citation.authorGórriz, M.; Antony, J.; McGuinness, K.; Giro, X.; O'Connor, N.
local.citation.contributorInternational conference on Medical Imaging with Deep Learning
local.citation.publicationNameInternational Conference on Medical Imaging with Deep Learning: 8-10 July 2019, London, United Kingdom: proceedings of Machine Learning Research
local.citation.startingPage197
local.citation.endingPage214


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