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dc.contributor.authorRomero-Lopez, Adrià
dc.contributor.authorGiró Nieto, Xavier
dc.contributor.authorBurdick, Jack
dc.contributor.authorMarques, Oge
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.identifier.citationRomero-Lopez, A., Giro, X., Burdick, J., Marques, O. Skin lesion classification from dermoscopic images using deep learning techniques. A: The IASTED International Conference on Biomedical Engineering. "Biomedical engineering 2017". Innsbruck: ACTA Press, 2017, p. 1-6.
dc.description.abstractThe recent emergence of deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist the human expert in making better decisions about a patient’s health. In this paper we focus on the problem of skin lesion classification, particularly early melanoma detection, and present a deep-learning based approach to solve the problem of classifying a dermoscopic image containing a skin lesion as malignant or benign. The proposed solution is built around the VGGNet convolutional neural network architecture and uses the transfer learning paradigm. Experimental results are encouraging: on the ISIC Archive dataset, the proposed method achieves a sensitivity value of 78.66%, which is significantly higher than the current state of the art on that dataset.
dc.format.extent6 p.
dc.publisherACTA Press
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Reconeixement de formes
dc.subjectÀrees temàtiques de la UPC::Ciències de la salut::Medicina::Dermatologia
dc.subject.lcshDiagnostic imaging
dc.subject.lcshImage processing--Digital techniques
dc.subject.othermedical image analysis
dc.subject.otherdeep learning
dc.subject.othermachine learning
dc.titleSkin lesion classification from dermoscopic images using deep learning techniques
dc.typeConference lecture
dc.subject.lemacDiagnòstic per la imatge
dc.subject.lemacImatges mèdiques
dc.subject.lemacImatges -- Processament -- Tècniques digitals
dc.contributor.groupUniversitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
upcommons.citation.authorRomero-Lopez, A., Giro, X., Burdick, J., Marques, O.
upcommons.citation.contributorThe IASTED International Conference on Biomedical Engineering
upcommons.citation.publicationNameBiomedical engineering 2017

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