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Deep Learning in Healthcare. Paradigms and Applications / edited by Yen-Wei Chen, Lakhmi C. Jain.

Contributor(s): SpringerLink (Online service) | Chen, Yen-Wei, editor | Jain, Lakhmi C., editor
Material type: materialTypeLabelE-bookSeries: (Intelligent Systems Reference Library, 1868-4394; 171); (Intelligent Technologies and Robotics (Springer-42732)).Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: 1 recurso en línea (XIV, 218 páginas) : 114 ilustraciones, 90 ilustraciones a color..ISBN: 9783030326067.Subject: Inteligencia artificial en medicinaOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Medical Image Detection Using Deep Learning -- Medical Image Segmentation Using Deep Learning -- Medical Image Classification Using Deep Learning.
In: Springer eBooksAbstract: This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems. Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data. Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería R859.7.A78 2020 EB (Browse shelf(Opens below)) Acceso electrónico eBook04032122
Total holds: 0

Medical Image Detection Using Deep Learning -- Medical Image Segmentation Using Deep Learning -- Medical Image Classification Using Deep Learning.

This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems. Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data. Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.

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