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Medical Image Understanding and Analysis

Overview of attention for book
Cover of 'Medical Image Understanding and Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 A Multi-resolution Deep Learning Framework for Lung Adenocarcinoma Growth Pattern Classification
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    Chapter 2 Convolutional Neural Networks for Efficient Localization of Interstitial Lung Disease Patterns in HRCT Images
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    Chapter 3 Segmentating Nucleus Membranes in SBFSEM Volume Data with Deep Neural Networks
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    Chapter 4 Novel Quantitative Magnetic Resonance Imaging Features with Liver Function Tests to Distinguish Parenchymal and Biliary Disease
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    Chapter 5 Regional Assessment of Liver Disease Progression and Response to Therapy by Multi-time Point m-SLIC Correspondence
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    Chapter 6 Automated Detection of Cystic Lesions in Quantitative T1 Liver Images
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    Chapter 7 Voxel-Wise Analysis of Paediatric Liver MRI
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    Chapter 8 Quantitative Evaluation of Correction Methods and Simulation of Motion Artifacts for Rotary Pullback Imaging Catheters
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    Chapter 9 A Voting-Based Encoding Technique for the Classification of Gleason Score for Prostate Cancers
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    Chapter 10 Segmentation of Lung Field in HRCT Images Using U-Net Based Fully Convolutional Networks
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    Chapter 11 Identifying Shape-Based Biomarkers for Diagnosis of Parkinson’s Disease from Ioflupane ( 123 I) SPECT Data
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    Chapter 12 Features for the Detection of Flat Polyps in Colonoscopy Video
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    Chapter 13 Feature Analysis of Biomarker Descriptors for HER2 Classification of Histology Slides
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    Chapter 14 Analysis of the Symmetry of Electrodes for Electropalatography with Cone Beam CT Scanning
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    Chapter 15 Breast Tissue Classification Using Local Binary Pattern Variants: A Comparative Study
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    Chapter 16 Volumetric Texture Analysis Based on Three-Dimensional Gaussian Markov Random Fields for COPD Detection
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    Chapter 17 Texture Descriptors for Classifying Sparse, Irregularly Sampled Optical Endomicroscopy Images
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    Chapter 18 What’s in a Smile? Initial Results of Multilevel Principal Components Analysis of Facial Shape and Image Texture
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    Chapter 19 An Automated Age-Related Macular Degeneration Classification Based on Local Texture Features in Optical Coherence Tomography Angiography
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    Chapter 20 Developing a Framework for Studying Brain Networks in Neonatal Hypoxic-Ischemic Encephalopathy
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    Chapter 21 MRI Super-Resolution Using Multi-channel Total Variation
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    Chapter 22 Manifold Modeling of the Beating Heart Motion
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    Chapter 23 Automated Segmentation of HeLa Nuclear Envelope from Electron Microscopy Images
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    Chapter 24 Automatic Segmentation of Microcalcification Clusters
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    Chapter 25 Analysis of the Interactions of Migrating Macrophages
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    Chapter 26 An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation
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    Chapter 27 A Comparison of Unsupervised Abnormality Detection Methods for Interstitial Lung Disease
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    Chapter 28 SoftCut: A Virtual Planning Tool for Soft Tissue Resection on CT Images
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    Chapter 29 Simultaneous Segmentation of Multiple Retinal Pathologies Using Fully Convolutional Deep Neural Network
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    Chapter 30 Improving the Resolution of Retinal OCT with Deep Learning
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    Chapter 31 An Improved U-Net Architecture for Simultaneous Arteriole and Venule Segmentation in Fundus Image
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    Chapter 32 Image Analysis in Light Sheet Fluorescence Microscopy Images of Transgenic Zebrafish Vascular Development
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    Chapter 33 Color Correction of Baby Images for Cyanosis Detection
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    Chapter 34 Deep Autoencoder Features for Registration of Histology Images
Overall attention for this book and its chapters
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Mentioned by

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9 tweeters

Citations

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Readers on

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80 Mendeley
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Title
Medical Image Understanding and Analysis
Published by
Springer International Publishing, October 2018
DOI 10.1007/978-3-319-95921-4
ISBNs
978-3-31-995920-7, 978-3-31-995921-4
Editors

Nixon, Mark, Mahmoodi, Sasan, Zwiggelaar, Reyer

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Unknown 79 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 31%
Student > Master 13 16%
Researcher 11 14%
Student > Bachelor 6 8%
Student > Doctoral Student 4 5%
Other 5 6%
Unknown 16 20%
Readers by discipline Count As %
Computer Science 28 35%
Engineering 15 19%
Medicine and Dentistry 3 4%
Agricultural and Biological Sciences 3 4%
Neuroscience 2 3%
Other 8 10%
Unknown 21 26%