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dc.contributor.authorSánchez-Peralta, Luisa F.
dc.contributor.authorPicón, Artzai
dc.contributor.authorAntequera-Barroso, Juan Antonio
dc.contributor.authorOrtega-Morán, Juan Francisco
dc.contributor.authorSánchez-Margallo, Francisco M.
dc.contributor.authorPagador, J. Blas
dc.date.accessioned2020-08-28T14:40:30Z
dc.date.available2020-08-28T14:40:30Z
dc.date.issued2020
dc.identifier.citationSánchez-Peralta, Luisa F., Artzai Picón, Juan Antonio Antequera-Barroso, Juan Francisco Ortega-Morán, Francisco M. Sánchez-Margallo, and J. Blas Pagador. “Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation.” Mathematics 8, no. 8 (August 7, 2020): 1316. doi:10.3390/math8081316.en
dc.identifier.urihttp://hdl.handle.net/11556/963
dc.description.abstractColorectal cancer is one of the leading cancer death causes worldwide, but its early diagnosis highly improves the survival rates. The success of deep learning has also benefited this clinical field. When training a deep learning model, it is optimized based on the selected loss function. In this work, we consider two networks (U-Net and LinkNet) and two backbones (VGG-16 and Densnet121). We analyzed the influence of seven loss functions and used a principal component analysis (PCA) to determine whether the PCA-based decomposition allows for the defining of the coefficients of a non-redundant primal loss function that can outperform the individual loss functions and different linear combinations. The eigenloss is defined as a linear combination of the individual losses using the elements of the eigenvector as coefficients. Empirical results show that the proposed eigenloss improves the general performance of individual loss functions and outperforms other linear combinations when Linknet is used, showing potential for its application in polyp segmentation problems.en
dc.language.isoengen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleEigenloss: Combined PCA-Based Loss Function for Polyp Segmentationen
dc.typearticleen
dc.identifier.doi10.3390/math8081316en
dc.rights.accessRightsopenAccessen
dc.subject.keywordsDeep learningen
dc.subject.keywordsLoss functionsen
dc.subject.keywordsPrincipal component analysisen
dc.subject.keywordsPolyp segmentationen
dc.identifier.essn2227-7390en
dc.issue.number8en
dc.journal.titleMathematicsen
dc.page.initial1316en
dc.volume.number8en


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