Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation

Luisa F. Sánchez-Peralta, Artzai Picón, Juan Antonio Antequera-Barroso, Juan Francisco Ortega-Morán, Francisco M. Sánchez-Margallo and J. Blas Pagador
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Luisa F. Sánchez-Peralta: Jesús Usón Minimally Invasive Surgery Centre, N-521, km 41.7, E-10071 Cáceres, Spain
Artzai Picón: TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, C/Geldo. Edificio 700, E-48160 Derio, Spain
Juan Antonio Antequera-Barroso: Didactics of Mathematics, University of Cadiz, Avda. República Saharaui s/n. Campus de Puerto Real, E-11519 Puerto Real, Spain
Juan Francisco Ortega-Morán: Jesús Usón Minimally Invasive Surgery Centre, N-521, km 41.7, E-10071 Cáceres, Spain
Francisco M. Sánchez-Margallo: Jesús Usón Minimally Invasive Surgery Centre, N-521, km 41.7, E-10071 Cáceres, Spain
J. Blas Pagador: Jesús Usón Minimally Invasive Surgery Centre, N-521, km 41.7, E-10071 Cáceres, Spain

Mathematics, 2020, vol. 8, issue 8, 1-19

Abstract: Colorectal 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.

Keywords: deep learning; loss functions; principal component analysis; polyp segmentation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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