Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation: Combined PCA-based loss function for polyp segmentation

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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.contributor.institutionCOMPUTER_VISION
dc.date.issued2020-08
dc.descriptionPublisher Copyright: © 2020 by the authors.
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.description.statusPeer reviewed
dc.format.extent1
dc.format.extent2067169
dc.identifier.citationSánchez-Peralta , L F , Picón , A , Antequera-Barroso , J A , Ortega-Morán , J F , Sánchez-Margallo , F M & Pagador , J B 2020 , ' Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation : Combined PCA-based loss function for polyp segmentation ' , Mathematics , vol. 8 , no. 8 , 1316 , pp. 1316 . https://doi.org/10.3390/math8081316
dc.identifier.doi10.3390/math8081316
dc.identifier.issn2227-7390
dc.identifier.otherresearchoutputwizard: 11556/963
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85089736346&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofMathematics
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsDeep learning
dc.subject.keywordsLoss functions
dc.subject.keywordsPrincipal component analysis
dc.subject.keywordsPolyp segmentation
dc.subject.keywordsDeep learning
dc.subject.keywordsLoss functions
dc.subject.keywordsPrincipal component analysis
dc.subject.keywordsPolyp segmentation
dc.subject.keywordsComputer Science (miscellaneous)
dc.subject.keywordsGeneral Mathematics
dc.subject.keywordsEngineering (miscellaneous)
dc.subject.keywordsSDG 3 - Good Health and Well-being
dc.titleEigenloss: Combined PCA-Based Loss Function for Polyp Segmentation: Combined PCA-based loss function for polyp segmentationen
dc.typejournal article
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