Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Semantic deep learning"'
Autor:
Mercedes Arguello-Casteleiro, Robert Stevens, Julio Des-Diz, Chris Wroe, Maria Jesus Fernandez-Prieto, Nava Maroto, Diego Maseda-Fernandez, George Demetriou, Simon Peters, Peter-John M. Noble, Phil H. Jones, Jo Dukes-McEwan, Alan D. Radford, John Keane, Goran Nenadic
Publikováno v:
Journal of Biomedical Semantics, Vol 10, Iss S1, Pp 1-28 (2019)
Abstract Background Deep Learning opens up opportunities for routinely scanning large bodies of biomedical literature and clinical narratives to represent the meaning of biomedical and clinical terms. However, the validation and integration of this k
Externí odkaz:
https://doaj.org/article/032fc0428bee42aebfecfd98fd215865
Publikováno v:
Mathematics, Vol 10, Iss 23, p 4526 (2022)
Current mainstream deep learning methods for object detection are generally trained on high-quality datasets, which might have inferior performances under bad weather conditions. In the paper, a joint semantic deep learning algorithm is proposed to a
Externí odkaz:
https://doaj.org/article/d1e8f21bd56549688730a0a686294382
Autor:
Mercedes Arguello Casteleiro, George Demetriou, Warren Read, Maria Jesus Fernandez Prieto, Nava Maroto, Diego Maseda Fernandez, Goran Nenadic, Julie Klein, John Keane, Robert Stevens
Publikováno v:
Journal of Biomedical Semantics, Vol 9, Iss 1, Pp 1-24 (2018)
Abstract Background Automatic identification of term variants or acceptable alternative free-text terms for gene and protein names from the millions of biomedical publications is a challenging task. Ontologies, such as the Cardiovascular Disease Onto
Externí odkaz:
https://doaj.org/article/58c2e38662004bfd85f0b2e5004baecc
Akademický článek
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Autor:
Nava Maroto, Maria Jesus Fernandez Prieto, Julio Des Diz, Mercedes Arguello Casteleiro, Simon Peters, Chris Wroe, Robert Stevens, Diego Maseda Fernandez, Carlos Sevillano Torrado
Publikováno v:
JMIR Medical Informatics, Vol 8, Iss 8, p e16948 (2020)
Arguello Casteleiro, M, Des-Diz, J, Maroto, N, Fernandez-Prieto, M J, Peters, S, Wroe, C, Torrado, C S, Fernandez, D M & Stevens, R 2020, ' Semantic Deep Learning: prior knowledge and a type of four-term embedding analogies to acquire treatments for wellknown diseases ', JMIR medical informatics, vol. 8, no. 8 . https://doi.org/10.2196/16948
JMIR Medical Informatics
Arguello Casteleiro, M, Des-Diz, J, Maroto, N, Fernandez-Prieto, M J, Peters, S, Wroe, C, Torrado, C S, Fernandez, D M & Stevens, R 2020, ' Semantic Deep Learning: prior knowledge and a type of four-term embedding analogies to acquire treatments for wellknown diseases ', JMIR medical informatics, vol. 8, no. 8 . https://doi.org/10.2196/16948
JMIR Medical Informatics
Background How to treat a disease remains to be the most common type of clinical question. Obtaining evidence-based answers from biomedical literature is difficult. Analogical reasoning with embeddings from deep learning (embedding analogies) may ext
Autor:
George Demetriou, Mercedes Arguello-Casteleiro, Phil H. Jones, Chris Wroe, John A. Keane, M.J. Fernandez-Prieto, Julio Des-Diz, Peter-John M. Noble, Simon Peters, Nava Maroto, Goran Nenadic, Jo Dukes-McEwan, Alan D Radford, Diego Maseda-Fernandez, Robert Stevens
Publikováno v:
Journal of Biomedical Semantics
Arguello Casteleiro, M, Stevens, R, Des-Diz, J, Wroe, C, Fernandez-Prieto, M J, Maroto, N, Maseda Fernandez, D, Demetriou, G, Peters, S, Noble, P-J M, Jones, P H, Dukes-McEwan, J, Radford, A D, Keane, J & Nenadic, G 2019, ' Exploring semantic deep learning for building reliable and reusable one health knowledge from PubMed systematic reviews and veterinary clinical notes ', Journal of Biomedical Semantics, vol. 10, no. 22 . https://doi.org/10.1186/s13326-019-0212-6
Journal of Biomedical Semantics, Vol 10, Iss S1, Pp 1-28 (2019)
Arguello Casteleiro, M, Stevens, R, Des-Diz, J, Wroe, C, Fernandez-Prieto, M J, Maroto, N, Maseda Fernandez, D, Demetriou, G, Peters, S, Noble, P-J M, Jones, P H, Dukes-McEwan, J, Radford, A D, Keane, J & Nenadic, G 2019, ' Exploring semantic deep learning for building reliable and reusable one health knowledge from PubMed systematic reviews and veterinary clinical notes ', Journal of Biomedical Semantics, vol. 10, no. 22 . https://doi.org/10.1186/s13326-019-0212-6
Journal of Biomedical Semantics, Vol 10, Iss S1, Pp 1-28 (2019)
Background Deep Learning opens up opportunities for routinely scanning large bodies of biomedical literature and clinical narratives to represent the meaning of biomedical and clinical terms. However, the validation and integration of this knowledge
Autor:
Maria Jesus Fernandez Prieto, George Demetriou, Nava Maroto, Robert Stevens, Diego Maseda Fernandez, Warren J. Read, Goran Nenadic, Julie Klein, John A. Keane, Mercedes Arguello Casteleiro
Publikováno v:
Arguello Casteleiro, M, Demetriou, G, Read, W, Fernandez Prieto, M J, Maroto, N, Maseda Fernandez, D, Nenadic, G, Klein, J, Keane, J & Stevens, R 2018, ' Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature ', Journal of Biomedical Semantics, vol. 9, no. 13 . https://doi.org/10.1186/s13326-018-0181-1
Journal of Biomedical Semantics
Journal of Biomedical Semantics, Vol 9, Iss 1, Pp 1-24 (2018)
Journal of Biomedical Semantics
Journal of Biomedical Semantics, Vol 9, Iss 1, Pp 1-24 (2018)
Background\ud Automatic identification of term variants or acceptable alternative free-text terms for gene and protein names from the millions of biomedical publications is a challenging task. Ontologies, such as the Cardiovascular Disease Ontology (
Akademický článek
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Akademický článek
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Autor:
Vilalta Arias, Armand, Garcia Gasulla, Dario|||0000-0001-6732-5641, Parés Pont, Ferran, Moreno Vázquez, Jonatan, Ayguadé Parra, Eduard|||0000-0002-5146-103X, Labarta Mancho, Jesús José|||0000-0002-7489-4727, Cortés García, Claudio Ulises|||0000-0003-0192-3096, Suzumura, Toyotaro
Publikováno v:
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Universitat Politècnica de Catalunya (UPC)
The current state-of-the-art for image annotation and image retrieval tasks is obtained through deep neural networks, which combine an image representation and a text representation into a shared embedding space. In this paper we evaluate the impact
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d281f020c9ad0066bd66f00939530df
http://arxiv.org/abs/1707.09872
http://arxiv.org/abs/1707.09872