Deep Learning in the Biomedical Applications: Recent and Future Status
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Cell Image Analysis Techniques · Machine Learning in Materials Science · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
CEDRIC Laboratory of the Conservatoire National des Arts et métiers (CNAM), HESAM Université, 292, rue Saint-Martin, 750141 Paris CEDEX 03, France
FEMTO-ST, University of Bourgogne-Franche-Comté, 15B avenue des Montboucons, 25030 Besançon CEDEX, France
Sorbonne University, 4 Place Jussieu, 75005 Paris, France
Scientific Director of Orqual Group, Kitview, 65 bd. Niels Bohr, 69100 Villeurbanne, France
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