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O. Rodriguez; J. Mauri; E Fernandez-Nofrerias; A. Tovar; R. Villuendas; V. Valle; Oriol Pujol; Petia Radeva |
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Analisis de texturas mediante la modificacion de un modelo binario local para la segmentacion automatica de secuencias de ecografia intracoronaria |
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2003 |
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Revista Española de Cardiologia (IF: 0.959), 56(2), Congreso de las Enfermedades Cardiovasculares |
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Sevilla (Spain) |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ RMF2003f |
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413 |
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O. Rodriguez; J. Mauri; E Fernandez-Nofrerias; J. Lopez; A. Tovar; V. Valle; David Rotger; Petia Radeva |
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Title |
Cuantificacion tridimensional de la longitud de segmentos coronarios a partir de secuencias de ecografia intracoronaria |
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2003 |
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Revista Española de Cardiologia (IF: 0.959), 56(2), Congreso de las Enfermedades Cardiovasculares |
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Sevilla (Spain) |
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MILAB |
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BCNPCL @ bcnpcl @ RMF2003g |
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414 |
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O. Rodriguez; J. Mauri; E Fernandez-Nofrerias; J. Lopez; A. Tovar; R. Villuendas; V. Valle; Misael Rosales; Petia Radeva |
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Title |
Modelo fisico para la simulacion de imagenes de ecografia intracoronaria |
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2003 |
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Revista Española de Cardiologia (IF: 0.959), 56(2), Congreso de las Enfermedades Cardiovasculares |
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Sevilla (Spain) |
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MILAB |
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BCNPCL @ bcnpcl @ RMF2003h |
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415 |
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Author |
Eduardo Aguilar; Bhalaji Nagarajan; Beatriz Remeseiro; Petia Radeva |
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Title |
Bayesian deep learning for semantic segmentation of food images |
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2022 |
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Computers and Electrical Engineering |
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CEE |
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103 |
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108380 |
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Keywords |
Deep learning; Uncertainty quantification; Bayesian inference; Image segmentation; Food analysis |
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Abstract |
Deep learning has provided promising results in various applications; however, algorithms tend to be overconfident in their predictions, even though they may be entirely wrong. Particularly for critical applications, the model should provide answers only when it is very sure of them. This article presents a Bayesian version of two different state-of-the-art semantic segmentation methods to perform multi-class segmentation of foods and estimate the uncertainty about the given predictions. The proposed methods were evaluated on three public pixel-annotated food datasets. As a result, we can conclude that Bayesian methods improve the performance achieved by the baseline architectures and, in addition, provide information to improve decision-making. Furthermore, based on the extracted uncertainty map, we proposed three measures to rank the images according to the degree of noisy annotations they contained. Note that the top 135 images ranked by one of these measures include more than half of the worst-labeled food images. |
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October 2022 |
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Science Direct |
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Admin @ si @ ANR2022 |
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3763 |
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Author |
M.Gomez; J.Mauri; E.Fernandez-Nofrerias; O. Rodriguez-Leor; C Julia; Misael Rosales; Petia Radeva |
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Title |
Modelo fisico para la simulacion de ultrasonido Intravascular. |
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2002 |
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XXXVIII Congreso Nacional de la Sociedad Española de Cardiologia. |
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Madrid, Espanya |
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MILAB |
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BCNPCL @ bcnpcl @ GMF2002b |
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308 |
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