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Deep Learning in Medical Image Analysis

Overview of attention for book
Cover of 'Deep Learning in Medical Image Analysis'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Deep Learning in Medical Image Analysis
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    Chapter 2 Medical Image Synthesis via Deep Learning
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    Chapter 3 Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation
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    Chapter 4 Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram
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    Chapter 5 Decision Support System for Lung Cancer Using PET/CT and Microscopic Images
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    Chapter 6 Lesion Image Synthesis Using DCGANs for Metastatic Liver Cancer Detection
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    Chapter 7 Retinopathy Analysis Based on Deep Convolution Neural Network
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    Chapter 8 Diagnosis of Glaucoma on Retinal Fundus Images Using Deep Learning: Detection of Nerve Fiber Layer Defect and Optic Disc Analysis
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    Chapter 9 Automatic Segmentation of Multiple Organs on 3D CT Images by Using Deep Learning Approaches
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    Chapter 10 Techniques and Applications in Skin OCT Analysis
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    Chapter 11 Deep Learning Technique for Musculoskeletal Analysis
Attention for Chapter 8: Diagnosis of Glaucoma on Retinal Fundus Images Using Deep Learning: Detection of Nerve Fiber Layer Defect and Optic Disc Analysis
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Citations

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42 Dimensions

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31 Mendeley
Summary Dimensions citations
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Chapter title
Diagnosis of Glaucoma on Retinal Fundus Images Using Deep Learning: Detection of Nerve Fiber Layer Defect and Optic Disc Analysis
Chapter number 8
Book title
Deep Learning in Medical Image Analysis
Published by
Springer, Cham, February 2020
DOI 10.1007/978-3-030-33128-3_8
Pubmed ID
32030667
Book ISBNs
978-3-03-033127-6, 978-3-03-033128-3
Authors

Chisako Muramatsu, Muramatsu, Chisako

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 13%
Student > Postgraduate 3 10%
Professor 2 6%
Other 2 6%
Student > Ph. D. Student 2 6%
Other 4 13%
Unknown 14 45%
Readers by discipline Count As %
Computer Science 10 32%
Medicine and Dentistry 3 10%
Nursing and Health Professions 2 6%
Linguistics 1 3%
Unknown 15 48%

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