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dc.contributor.authorCombalia Escudero, Marc
dc.contributor.authorPérez Ankar, Javiera
dc.contributor.authorGarcía Herrera, Adriana
dc.contributor.authorAlos, Llúcia
dc.contributor.authorVilaplana Besler, Verónica
dc.contributor.authorMarqués Acosta, Fernando
dc.contributor.authorPuig, Susana
dc.contributor.authorMalvehy, Josep
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2019-09-04T07:18:31Z
dc.date.available2019-09-04T07:18:31Z
dc.date.issued2019
dc.identifier.citationCombalia Escudero, M. [et al.]. Digitally stained confocal microscopy through deep learning. 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". Brookline, MA: Microtome Publishing, 2019, p. 121-129.
dc.identifier.isbn2640-3498
dc.identifier.otherhttps://openreview.net/group?id=MIDL.io/2019/Conference
dc.identifier.urihttp://hdl.handle.net/2117/167907
dc.description.abstractSpecialists have used confocal microscopy in the ex-vivo modality to identify Basal Cell Carcinoma tumors with an overall sensitivity of 96.6% and specificity of 89.2% (Chung et al., 2004). However, this technology hasn’t established yet in the standard clinical practice because most pathologists lack the knowledge to interpret its output. In this paper we propose a combination of deep learning and computer vision techniques to digitally stain confocal microscopy images into H&E-like slides, enabling pathologists to interpret these images without specific training. We use a fully convolutional neural network with a multiplicative residual connection to denoise the confocal microscopy images, and then stain them using a Cycle Consistency Generative Adversarial Network
dc.format.extent9 p.
dc.language.isoeng
dc.publisherMicrotome Publishing
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
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::Ciències de la salut::Medicina::Diagnòstic per la imatge
dc.subject.lcshMicroscopy, Confocal
dc.subject.lcshImaging systems in medicine
dc.subject.lcshDeep learning
dc.subject.lcshNeural networks (Computer science)
dc.subject.otherDeep learning
dc.subject.otherNeural networks
dc.subject.otherDigital staining
dc.subject.otherConfocal microscopy
dc.subject.otherSpeckle noise
dc.subject.otherCycleGAN
dc.titleDigitally stained confocal microscopy through deep learning
dc.typeConference report
dc.subject.lemacMicroscòpia clínica
dc.subject.lemacImatges mèdiques
dc.subject.lemacAprenentatge profund
dc.subject.lemacXarxes neuronals (Informàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://proceedings.mlr.press/
dc.rights.accessOpen Access
local.identifier.drac25804842
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/2PE/ TEC2016-75976-R
local.citation.authorCombalia Escudero, Marc; Pérez, J.; García, A.; Alos, L.; Vilaplana, V.; Marques, F.; Puig, S.; Malvehy, J.
local.citation.contributorInternational Conference on Medical Imaging with Deep Learning
local.citation.pubplaceBrookline, MA
local.citation.publicationNameInternational Conference on Medical Imaging with Deep Learning: 8-10 July 2019, London, United Kingdom: proceedings of Machine Learning Research
local.citation.startingPage121
local.citation.endingPage129


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