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Open Access 01-12-2021 | Artificial Intelligence | Educational Review

A primer on deep learning and convolutional neural networks for clinicians

Authors: Lara Lloret Iglesias, Pablo Sanz Bellón, Amaia Pérez del Barrio, Pablo Menéndez Fernández-Miranda, David Rodríguez González, José A. Vega, Andrés A. González Mandly, José A. Parra Blanco

Published in: Insights into Imaging | Issue 1/2021

Abstract

Deep learning is nowadays at the forefront of artificial intelligence. More precisely, the use of convolutional neural networks has drastically improved the learning capabilities of computer vision applications, being able to directly consider raw data without any prior feature extraction. Advanced methods in the machine learning field, such as adaptive momentum algorithms or dropout regularization, have dramatically improved the convolutional neural networks predicting ability, outperforming that of conventional fully connected neural networks. This work summarizes, in an intended didactic way, the main aspects of these cutting-edge techniques from a medical imaging perspective.

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Title: A primer on deep learning and convolutional neural networks for clinicians
Authors: Lara Lloret Iglesias
Pablo Sanz Bellón
Amaia Pérez del Barrio
Pablo Menéndez Fernández-Miranda
David Rodríguez González
José A. Vega
Andrés A. González Mandly
José A. Parra Blanco
Publication date: 01-12-2021
Publisher: Springer International Publishing
Keyword: Artificial Intelligence
Published in: Insights into Imaging / Issue 1/2021
Electronic ISSN: 1869-4101
DOI: https://doi.org/10.1186/s13244-021-01052-z

Keynote webinar | Spotlight on medication adherence

Springer Medicine

A primer on deep learning and convolutional neural networks for clinicians

Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

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