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Deep Learning and Convolutional Neural Networks for Medical Image Computing

Overview of attention for book
Cover of 'Deep Learning and Convolutional Neural Networks for Medical Image Computing'

Table of Contents

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    Book Overview
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    Chapter 1 Deep Learning and Computer-Aided Diagnosis for Medical Image Processing: A Personal Perspective
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    Chapter 2 Review of Deep Learning Methods in Mammography, Cardiovascular, and Microscopy Image Analysis
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    Chapter 3 Efficient False Positive Reduction in Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation
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    Chapter 4 Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning
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    Chapter 5 A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set
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    Chapter 6 Deep Learning for Histopathological Image Analysis: Towards Computerized Diagnosis on Cancers
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    Chapter 7 Interstitial Lung Diseases via Deep Convolutional Neural Networks: Segmentation Label Propagation, Unordered Pooling and Cross-Dataset Learning
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    Chapter 8 Three Aspects on Using Convolutional Neural Networks for Computer-Aided Detection in Medical Imaging
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    Chapter 9 Cell Detection with Deep Learning Accelerated by Sparse Kernel
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    Chapter 10 Fully Convolutional Networks in Medical Imaging: Applications to Image Enhancement and Recognition
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    Chapter 11 On the Necessity of Fine-Tuned Convolutional Neural Networks for Medical Imaging
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    Chapter 12 Fully Automated Segmentation Using Distance Regularised Level Set and Deep-Structured Learning and Inference
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    Chapter 13 Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms
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    Chapter 14 Deep Learning Based Automatic Segmentation of Pathological Kidney in CT: Local Versus Global Image Context
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    Chapter 15 Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders
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    Chapter 16 Automatic Pancreas Segmentation Using Coarse-to-Fine Superpixel Labeling
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    Chapter 17 Interleaved Text/Image Deep Mining on a Large-Scale Radiology Image Database
Overall attention for this book and its chapters
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Mentioned by

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16 tweeters
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1 Facebook page

Citations

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54 Dimensions

Readers on

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119 Mendeley
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Title
Deep Learning and Convolutional Neural Networks for Medical Image Computing
Published by
Springer International Publishing, January 2017
DOI 10.1007/978-3-319-42999-1
ISBNs
978-3-31-942999-1, 978-3-31-942998-4
Editors

Le Lu, Yefeng Zheng, Gustavo Carneiro, Lin Yang

Twitter Demographics

The data shown below were collected from the profiles of 16 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 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 119 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 5%
Student > Master 3 3%
Unspecified 1 <1%
Student > Bachelor 1 <1%
Student > Doctoral Student 1 <1%
Other 2 2%
Unknown 105 88%
Readers by discipline Count As %
Engineering 4 3%
Computer Science 4 3%
Physics and Astronomy 2 2%
Social Sciences 1 <1%
Unspecified 1 <1%
Other 0 0%
Unknown 107 90%