Výsledky vyhledávání - "Ignacio Peis"

Akademický článek

A Heavy Tailed Expectation Maximization Hidden Markov Random Field Model with Applications to Segmentation of MRI

Autor: Diego Castillo-Barnes, Ignacio Peis, Francisco J. Martínez-Murcia, Fermín Segovia, Ignacio A. Illán, Juan M. Górriz, Javier Ramírez, Diego Salas-Gonzalez

Publikováno v: Frontiers in Neuroinformatics, Vol 11 (2017)

A wide range of segmentation approaches assumes that intensity histograms extracted from magnetic resonance images (MRI) have a distribution for each brain tissue that can be modeled by a Gaussian distribution or a mixture of them. Nevertheless, inte

Externí odkaz: https://doaj.org/article/8422dffaacfd4b7db7c6df21922790f9

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Unsupervised learning of global factors in deep generative models

Autor: Ignacio Peis, Pablo M. Olmos, Antonio Artés-Rodríguez

Publikováno v: Pattern Recognition. 134:109130

We present a novel deep generative model based on non i.i.d. variational autoencoders that captures global dependencies among observations in a fully unsupervised fashion. In contrast to the recent semi-supervised alternatives for global modeling in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1644119f042f1c748ef1afbeb5c90df4
https://doi.org/10.1016/j.patcog.2022.109130

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Relationship between olive oil consumption and ankle-brachial pressure index in a population at high cardiovascular risk

Autor: Estefanía Toledo, José Lapetra, Julia Wärnberg, Antonio Garcia-Rios, Vanessa Díaz-González, Rosario Lloret-Macián, José Ignacio Peis, José Manuel Santos-Lozano, Guadalupe González-Mata, Edelys Crespo-Oliva, Sandra González-Palacios, M. Angeles Zulet, Raul Martinez-Lacruz, Tomás Ripoll-Vera, Clotilde Vázquez, J. Alfredo Martínez, Maria Isabel Ramos-Ballesta, Rubén Sánchez-Rodríguez, Luis Serra-Majem, Silvia Canudas, Josep Vidal, José J. Gaforio, José Carlos Fernández-García, Ramon Estruch, Meritxell López, Rocío Barragán, Nancy Babio, Maria Dolores Zomeño Fajardo, Jessica Vaquero-Luna, Miguel Ángel Martínez-González, Dora Romaguera, Angel Ríos, Lidia Daimiel, Ángel M. Alonso-Gómez, Pilar Matía-Martín, Pilar Buil-Cosiales, Karla-Alejandra Pérez-Vega, Jesús Vioque, Rosa Casas, Dolores Corella, Júlia Muñoz-Martínez, Pablo Hernández-Alonso, Miguel Ruiz-Canela, Ana Galera, Anai Moreno-Rodriguez, Xavier Pintó, Jordi Salas-Salvadó, Francisco J. Tinahones, Josep A. Tur, Cristina Sánchez-Quesada, Montserrat Fitó, Emilio Ros, Naomi Cano-Ibáñez

Publikováno v: Dipòsit Digital de la UB
Universidad de Barcelona
Digital.CSIC. Repositorio Institucional del CSIC
instname
Atherosclerosis
r-ISABIAL. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica y Sanitaria de Alicante

[Background and aims]: The aim of this study was to ascertain the association between the consumption of different categories of edible olive oils (virgin olive oils and olive oil) and olive pomace oil and ankle-brachial pressure index (ABI) in parti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92db4793cf692104069849ed0d75b144
https://hdl.handle.net/10668/16564

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Actigraphic recording of motor activity in depressed inpatients: a novel computational approach to prediction of clinical course and hospital discharge

Autor: M. Mercedes Perez-Rodriguez, Enrique Baca-García, Marta Ruiz-Gomez, Javier D. López-Moríñigo, María Luisa Barrigón, Ignacio Peis, Antonio Artés-Rodríguez

Publikováno v: Scientific Reports
Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
Biblos-e Archivo: Repositorio Institucional de la UAM
Universidad Autónoma de Madrid
Biblos-e Archivo. Repositorio Institucional de la UAM
instname

Depressed patients present with motor activity abnormalities, which can be easily recorded using actigraphy. The extent to which actigraphically recorded motor activity may predict inpatient clinical course and hospital discharge remains unknown. Par

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ad93fd5641a54953eec0ac521624a95

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Akademický článek

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Deep Sequential Models for Suicidal Ideation from Multiple Source Data

Autor: Philippe Courtet, Enrique Baca-García, María Luisa Barrigón, Constanza Vera-Varela, Pablo M. Olmos, Ignacio Peis, Antonio Artés-Rodríguez

Publikováno v: e-Archivo: Repositorio Institucional de la Universidad Carlos III de Madrid
Universidad Carlos III de Madrid (UC3M)
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics, Institute of Electrical and Electronics Engineers, 2019, 23 (6), pp.2286--2293. ⟨10.1109/JBHI.2019.2919270⟩
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname

This article presents a novel method for predicting suicidal ideation from Electronic Health Records (EHR) and Ecological Momentary Assessment (EMA) data using deep sequential models. Both EHR longitudinal data and EMA question forms are defined by a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d5e58816689b730a64da85f71bd9a9d
http://arxiv.org/abs/1911.03522

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A Heavy Tailed Expectation Maximization Hidden Markov Random Field Model with Applications to Segmentation of MRI

Autor: Javier Ramírez, Ignacio Peis, Ignacio A. Illán, Diego Salas-Gonzalez, Fermín Segovia, Diego Castillo-Barnes, Juan Manuel Górriz, Francisco Jesús Martínez-Murcia

Publikováno v: Frontiers in Neuroinformatics
Frontiers in Neuroinformatics, Vol 11 (2017)

A wide range of segmentation approaches assumes that intensity histograms extracted from magnetic resonance images (MRI) have a distribution for each brain tissue that can be modelled by a Gaussian distribution or a mixture of them. Nevertheless, int

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0373a1d60b12156691567e0818d88a64
https://doi.org/10.3389/fninf.2017.00066

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MRI brain segmentation using hidden Markov random fields with alpha-stable distributions

Autor: Diego Salas-Gonzalez, Francisco Jesús Martínez-Murcia, Elmar Lang, Javier Ramírez, Juan Manuel Górriz, Ignacio Peis, Fermín Segovia, Ignacio A. Illán

Publikováno v: 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD).

A MRI brain image segmentation method using a hidden Markov random fields with heavy-tailed alpha-stable distributions is presented. Each brain tissue is modelled using an alpha-stable distribution. Then, a HMRF is used to include spatial information

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ee39c8871b2457ea4c63dd1b3076aa50
https://doi.org/10.1109/nssmic.2016.8069422

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