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OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging

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Cover of 'OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging'

Table of Contents

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    Book Overview
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    Chapter 1 Feature Aggregation Decoder for Segmenting Laparoscopic Scenes
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    Chapter 2 Preoperative Planning for Guidewires Employing Shape-Regularized Segmentation and Optimized Trajectories
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    Chapter 3 Guided Unsupervised Desmoking of Laparoscopic Images Using Cycle-Desmoke
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    Chapter 4 Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration
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    Chapter 5 Live Monitoring of Haemodynamic Changes with Multispectral Image Analysis
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    Chapter 6 Towards a Cyber-Physical Systems Based Operating Room of the Future
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    Chapter 7 Deep Transfer Learning for Whole-Brain FMRI Analyses
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    Chapter 8 Knowledge Distillation for Semi-supervised Domain Adaptation
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    Chapter 9 Relevance Vector Machines for Harmonization of MRI Brain Volumes Using Image Descriptors
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    Chapter 10 Data Pooling and Sampling of Heterogeneous Image Data for White Matter Hyperintensity Segmentation
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    Chapter 11 A Hybrid 3DCNN and 3DC-LSTM Based Model for 4D Spatio-Temporal fMRI Data: An ABIDE Autism Classification Study
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    Chapter 12 Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI Across Sites
Attention for Chapter 7: Deep Transfer Learning for Whole-Brain FMRI Analyses
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Citations

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Chapter title
Deep Transfer Learning for Whole-Brain FMRI Analyses
Chapter number 7
Book title
OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging
Published by
Springer, Cham, October 2019
DOI 10.1007/978-3-030-32695-1_7
Book ISBNs
978-3-03-032694-4, 978-3-03-032695-1
Authors

Armin W. Thomas, Klaus-Robert Müller, Wojciech Samek, Thomas, Armin W., Müller, Klaus-Robert, Samek, Wojciech

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 48 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 19%
Researcher 7 15%
Professor 5 10%
Student > Master 4 8%
Student > Bachelor 2 4%
Other 4 8%
Unknown 17 35%
Readers by discipline Count As %
Computer Science 10 21%
Neuroscience 6 13%
Engineering 5 10%
Psychology 2 4%
Decision Sciences 1 2%
Other 3 6%
Unknown 21 44%

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