Multimodality imaging. Volume 1, Deep learning applications /

This research and reference text explores the finer details of deep learning models. It provides a brief outline on popular models including convolution neural networks, deep belief networks, autoencoders and residual neural networks.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Biswas, Mainak (Editor ), Suri, Jasjit S. (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2022]
Colección:IOP (Series). Release 22.
IOP ebooks. 2022 collection.
Temas:
Diagnostic imaging > Data processing.
Deep learning (Machine learning)
Computer vision.
Diagnostic Imaging.
Deep Learning.
Visual Perception.
Biomedical engineering.
TECHNOLOGY & ENGINEERING / Biomedical.
Acceso en línea:Texto completo
Descripción
Sumario:This research and reference text explores the finer details of deep learning models. It provides a brief outline on popular models including convolution neural networks, deep belief networks, autoencoders and residual neural networks.
Notas:"Version: 20221201"--Title page verso.
Descripción Física:1 online resource (various pagings) : illustrations (some color).
Also available in print.
Público:Academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis.
Bibliografía:Includes bibliographical references.
ISBN:9780750322447
9780750322430

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