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Appl. Sci. 2019, 9(8), 1526; https://doi.org/10.3390/app9081526

Deep Learning in the Biomedical Applications: Recent and Future Status

1
CEDRIC Laboratory of the Conservatoire National des Arts et métiers (CNAM), HESAM Université, 292, rue Saint-Martin, 750141 Paris CEDEX 03, France
2
FEMTO-ST, University of Bourgogne-Franche-Comté, 15B avenue des Montboucons, 25030 Besançon CEDEX, France
3
Sorbonne University, 4 Place Jussieu, 75005 Paris, France
4
Scientific Director of Orqual Group, Kitview, 65 bd. Niels Bohr, 69100 Villeurbanne, France
*
Author to whom correspondence should be addressed.
Received: 12 March 2019 / Revised: 8 April 2019 / Accepted: 9 April 2019 / Published: 12 April 2019
(This article belongs to the Special Issue Machine Learning for Biomedical Data Analysis)
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Abstract

Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain. In this domain, the different areas of interest concern the Omics (study of the genome—genomics—and proteins—transcriptomics, proteomics, and metabolomics), bioimaging (study of biological cell and tissue), medical imaging (study of the human organs by creating visual representations), BBMI (study of the brain and body machine interface) and public and medical health management (PmHM). This paper reviews the major deep learning concepts pertinent to such biomedical applications. Concise overviews are provided for the Omics and the BBMI. We end our analysis with a critical discussion, interpretation and relevant open challenges. View Full-Text
Keywords: deep neural networks; biomedical applications; Omics; medical imaging; brain and body machine interface deep neural networks; biomedical applications; Omics; medical imaging; brain and body machine interface
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zemouri, R.; Zerhouni, N.; Racoceanu, D. Deep Learning in the Biomedical Applications: Recent and Future Status. Appl. Sci. 2019, 9, 1526.

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