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Deep learning in healthcareparadigms...

Chen, Yen-Wei.

Deep learning in healthcareparadigms and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning in healthcareedited by Yen-Wei Chen, Lakhmi C. Jain.
Reminder of title:	paradigms and applications /
other author:	Chen, Yen-Wei.
Published:	Cham :Springer International Publishing :2020.
Description:	xiv, 218 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Artificial intelligenceMedical applications.
Online resource:	https://doi.org/10.1007/978-3-030-32606-7
ISBN:	9783030326067$q(electronic bk.)

Deep learning in healthcareparadigms and applications /
Deep learning in healthcareparadigms and applications /[electronic resource] :edited by Yen-Wei Chen, Lakhmi C. Jain. - Cham :Springer International Publishing :2020. - xiv, 218 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.1711868-4394 ;. - Intelligent systems reference library ;v.24..

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.

ISBN: 9783030326067$q(electronic bk.)

Standard No.: 10.1007/978-3-030-32606-7doiSubjects--Topical Terms:

237917
Artificial intelligence
--Medical applications.

LC Class. No.: R859.7.A78 / D447 2020

Dewey Class. No.: 610.28563

Deep learning in healthcareparadigms and applications /
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505 0 $a Medical Image Detection Using Deep Learning -- Medical Image Segmentation Using Deep Learning -- Medical Image Classification Using Deep Learning.
520 $a 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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