A primer on deep learning and convolutional neural networks for clinicians

Autor:	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
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	Deep learning Image processing Medical imaging Educational Medical physics. Medical radiology. Nuclear medicine R895-920
Zdroj:	Insights into Imaging, Vol 12, Iss 1, Pp 1-11 (2021)
Druh dokumentu:	article
ISSN:	1869-4101
DOI:	10.1186/s13244-021-01052-z
Popis:	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.
Databáze:	Directory of Open Access Journals
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