Zobrazeno 1 - 10
of 313
pro vyhledávání: '"Cabello, Miguel"'
Autor:
Jiménez-Partinen, Ariadna, Molina-Cabello, Miguel A., Thurnhofer-Hemsi, Karl, Palomo, Esteban J., Rodríguez-Capitán, Jorge, Molina-Ramos, Ana I., Jiménez-Navarro, Manuel
Coronary artery disease (CAD) remains the leading cause of death globally and invasive coronary angiography (ICA) is considered the gold standard of anatomical imaging evaluation when CAD is suspected. However, risk evaluation based on ICA has severa
Externí odkaz:
http://arxiv.org/abs/2402.00570
Autor:
Cabello, Miguel
Statistical identification of possibly non-fundamental SVARMA models requires structural errors: (i) to be an i.i.d process, (ii) to be mutually independent across components, and (iii) each of them must be non-Gaussian distributed. Hence, provided t
Externí odkaz:
http://arxiv.org/abs/2212.07263
Autor:
Rodríguez Cabello, Miguel Angel, Méndez Rubio, Santiago, Vázquez Alba, David, Aulló González, Carolina, Platas Sancho, Arturo
Publikováno v:
In Clinical Genitourinary Cancer October 2024 22(5)
Autor:
Sánchez-Ávila, N., Cardarelli, Alessandro, Carmona-Cabello, Miguel, Dorado, M.P., Pinzi, Sara, Barbanera, Marco
Publikováno v:
In Renewable Energy September 2024 230
Autor:
Calderon-Ramirez, Saul, Murillo-Hernandez, Diego, Rojas-Salazar, Kevin, Elizondo, David, Yang, Shengxiang, Molina-Cabello, Miguel
The implementation of deep learning based computer aided diagnosis systems for the classification of mammogram images can help in improving the accuracy, reliability, and cost of diagnosing patients. However, training a deep learning model requires a
Externí odkaz:
http://arxiv.org/abs/2107.11696
Autor:
Zamora-Cardenas, Willard, Mendez, Mauro, Calderon-Ramirez, Saul, Vargas, Martin, Monge, Gerardo, Quiros, Steve, Elizondo, David, Molina-Cabello, Miguel A.
Cell instance segmentation in fluorescence microscopy images is becoming essential for cancer dynamics and prognosis. Data extracted from cancer dynamics allows to understand and accurately model different metabolic processes such as proliferation. T
Externí odkaz:
http://arxiv.org/abs/2106.05843
Autor:
Ruiz-Casado, Jose L.1 (AUTHOR) joseruizcasado02@gmail.com, Molina-Cabello, Miguel A.1,2 (AUTHOR) miguelangel@lcc.uma.es, Luque-Baena, Rafael M.1,2 (AUTHOR) rmluque@uma.es
Publikováno v:
Sensors (14248220). Jun2024, Vol. 24 Issue 12, p3777. 19p.
Autor:
Calderon-Ramirez, Saul, Shengxiang-Yang, Moemeni, Armaghan, Elizondo, David, Colreavy-Donnelly, Simon, Chavarria-Estrada, Luis Fernando, Molina-Cabello, Miguel A.
The Corona Virus (COVID-19) is an internationalpandemic that has quickly propagated throughout the world. The application of deep learning for image classification of chest X-ray images of Covid-19 patients, could become a novel pre-diagnostic detect
Externí odkaz:
http://arxiv.org/abs/2008.08496
Support Vector Machines (SVMs) are still one of the most popular and precise classifiers. The Radial Basis Function (RBF) kernel has been used in SVMs to separate among classes with considerable success. However, there is an intrinsic dependence on t
Externí odkaz:
http://arxiv.org/abs/2007.08233
Autor:
Calderon-Ramirez, Saul, Oala, Luis, Torrents-Barrena, Jordina, Yang, Shengxiang, Moemeni, Armaghan, Samek, Wojciech, Molina-Cabello, Miguel A.
In this work, we propose MixMOOD - a systematic approach to mitigate effect of class distribution mismatch in semi-supervised deep learning (SSDL) with MixMatch. This work is divided into two components: (i) an extensive out of distribution (OOD) abl
Externí odkaz:
http://arxiv.org/abs/2006.07767