Deep learning in healthcare : paradigms and applications / Yen-Wei Chen, Lakhmi C. Jain, editors.

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Deep learning in healthcare : paradigms and applications / Yen-Wei Chen, Lakhmi C. Jain, editors.

9783030326067 (electronic book)
3030326063 (electronic book)
3030326055
9783030326050

Cham : Springer, ©2020.

English

1 online resource (225 pages).

R859.7.A78

610.28563

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.

Includes bibliographical references and index.

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Online resource; title from PDF title page (SpringerLink, viewed December 4, 2019).

Intelligent systems reference library ; v. 171.

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