Preferred Label : Deep Learning;
MeSH definition : Supervised or unsupervised machine learning methods that use multiple layers of data
representations generated by nonlinear transformations, instead of individual task-specific
ALGORITHMS, to build and train neural network models.;
Définition CISMeF : A specialized branch of ML that involves training neural networks with multiple intermediary
(hidden) layers that operate between an input layer that receives data and an output
layer that presents the final network output. Each layer learns to transform its input
data into a slightly more abstract and composite representation and produces an output
that serves as an input for the next layer. As data propagates through successive
layers, these models are able to learn hierarchical feature representations from the
input data. For example, in healthcare, deep learning models can be used to identify
tumors or suspicious lesions in medical images to support physicians and radiologists
in the evaluation of disease.; Source: Adapted from: Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo,
M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep
learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-zExternal
Link Disclaimer International Organization for Standardization. (2020). Software and
systems engineering — Software testing — Part 11: Guidelines on the testing of AI-based
systems (ISO/IEC TR 29119-11:2020). https://www.iso.org/standard/79016.html;
MeSH synonym : Learning, Deep; Hierarchical Learning; Learning, Hierarchical;
CISMeF synonym : deep structured learning; hierarchical learning;
Origin ID : D000077321;
UMLS CUI : C4704761;
Allowable qualifiers
CISMeF manual mappings
Record concept(s)
Semantic type(s)
UMLS correspondences (same concept)
Supervised or unsupervised machine learning methods that use multiple layers of data
representations generated by nonlinear transformations, instead of individual task-specific
ALGORITHMS, to build and train neural network models.
A specialized branch of ML that involves training neural networks with multiple intermediary
(hidden) layers that operate between an input layer that receives data and an output
layer that presents the final network output. Each layer learns to transform its input
data into a slightly more abstract and composite representation and produces an output
that serves as an input for the next layer. As data propagates through successive
layers, these models are able to learn hierarchical feature representations from the
input data. For example, in healthcare, deep learning models can be used to identify
tumors or suspicious lesions in medical images to support physicians and radiologists
in the evaluation of disease.
Source: Adapted from: Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo,
M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep
learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-zExternal
Link Disclaimer International Organization for Standardization. (2020). Software and
systems engineering — Software testing — Part 11: Guidelines on the testing of AI-based
systems (ISO/IEC TR 29119-11:2020). https://www.iso.org/standard/79016.html
https://dumas.ccsd.cnrs.fr/dumas-02100870
2018
France
dissertations, academic
Learning
intelligence, nos
Learning
Artificial intelligence
cytogenetics
learning, nos
Applications
Cytogenetics
Cytogenetics
Deep Learning
Applications
artificial intelligence
Applications
attention
artificial intelligence
---
https://youtu.be/7zpMVyAu84Q
https://sesstim.univ-amu.fr/video-box/webinar-sesstim-ohi-cecile-capponi
2018
false
false
false
false
false
France
audiovisual aids
Municipality
Communication
learning, nos
Communication
health communication
Learning
Communications; transports; civil engineering
Portal vein air
Deep Learning
Learning
Deep Learning
health communication
---
https://youtu.be/in-n6fiWJW8
https://sesstim.univ-amu.fr/video-box/webinar-sesstim-ohi-cecile-capponi-0
2018
false
false
false
false
false
France
audiovisual aids
Communication
Municipality
Communication
Learning
Deep Learning
learning, nos
health communication
Communications; transports; civil engineering
Learning
Deep Learning
health communication
---