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Deep learning in medical image analy...

Fujita, Hiroshi.

Deep learning in medical image analysischallenges and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning in medical image analysisedited by Gobert Lee, Hiroshi Fujita.
Reminder of title:	challenges and applications /
other author:	Lee, Gobert.
Published:	Cham :Springer International Publishing :2020.
Description:	viii, 181 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Artificial intelligenceMedical applications.
Online resource:	https://doi.org/10.1007/978-3-030-33128-3
ISBN:	9783030331283$q(electronic bk.)

Deep learning in medical image analysischallenges and applications /
Deep learning in medical image analysischallenges and applications /[electronic resource] :edited by Gobert Lee, Hiroshi Fujita. - Cham :Springer International Publishing :2020. - viii, 181 p. :ill., digital ;24 cm. - Advances in experimental medicine and biology,v.12130065-2598 ;. - Advances in experimental medicine and biology ;946..

Deep Learning in Medical Image Analysis -- Medical Image Synthesis via Deep Learning -- Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation -- Deep Learning Computer Aided Diagnosis for Breast Lesion in Digital Mammogram -- Decision support system for lung cancer using PET/CT and microscopic images -- Lesion Image Synthesis using DCGANs for Metastatic Liver Cancer Detection -- Retinopathy analysis based on deep convolution neural network -- Diagnosis of Glaucoma on retinal fundus images using deep learning: detection of nerve fiber layer defect and optic disc analysis -- Automatic segmentation of multiple organs on 3D CT images by using deep learning approaches -- Techniques and Applications in Skin OCT Analysis -- Deep Learning Technique for Musculoskeletal Analysis -- Index.

This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.

ISBN: 9783030331283$q(electronic bk.)

Standard No.: 10.1007/978-3-030-33128-3doiSubjects--Topical Terms:

237917
Artificial intelligence
--Medical applications.

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

Dewey Class. No.: 616.0754

Deep learning in medical image analysischallenges and applications /
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505 0 $a Deep Learning in Medical Image Analysis -- Medical Image Synthesis via Deep Learning -- Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation -- Deep Learning Computer Aided Diagnosis for Breast Lesion in Digital Mammogram -- Decision support system for lung cancer using PET/CT and microscopic images -- Lesion Image Synthesis using DCGANs for Metastatic Liver Cancer Detection -- Retinopathy analysis based on deep convolution neural network -- Diagnosis of Glaucoma on retinal fundus images using deep learning: detection of nerve fiber layer defect and optic disc analysis -- Automatic segmentation of multiple organs on 3D CT images by using deep learning approaches -- Techniques and Applications in Skin OCT Analysis -- Deep Learning Technique for Musculoskeletal Analysis -- Index.
520 $a This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.
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