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Understanding and Interpreting Machine Learning in Medical Image Computing Applications

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Cover of 'Understanding and Interpreting Machine Learning in Medical Image Computing Applications'

Table of Contents

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    Book Overview
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    Chapter 1 Alzheimer’s Disease Modelling and Staging Through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes
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    Chapter 2 Multi-channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer’s Disease
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    Chapter 3 Visualizing Convolutional Networks for MRI-Based Diagnosis of Alzheimer’s Disease
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    Chapter 4 Finding Effective Ways to (Machine) Learn fMRI-Based Classifiers from Multi-site Data
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    Chapter 5 Towards Robust CT-Ultrasound Registration Using Deep Learning Methods
  7. Altmetric Badge
    Chapter 6 To Learn or Not to Learn Features for Deformable Registration?
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    Chapter 7 Evaluation of Strategies for PET Motion Correction - Manifold Learning vs. Deep Learning
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    Chapter 8 Exploring Adversarial Examples
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    Chapter 9 Shortcomings of Ventricle Segmentation Using Deep Convolutional Networks
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    Chapter 10 Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks
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    Chapter 11 Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images
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    Chapter 12 Automatic Brain Tumor Grading from MRI Data Using Convolutional Neural Networks and Quality Assessment
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    Chapter 13 Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification
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    Chapter 14 Regression Concept Vectors for Bidirectional Explanations in Histopathology
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    Chapter 15 Towards Complementary Explanations Using Deep Neural Networks
  17. Altmetric Badge
    Chapter 16 How Users Perceive Content-Based Image Retrieval for Identifying Skin Images
Attention for Chapter 12: Automatic Brain Tumor Grading from MRI Data Using Convolutional Neural Networks and Quality Assessment
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Citations

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Chapter title
Automatic Brain Tumor Grading from MRI Data Using Convolutional Neural Networks and Quality Assessment
Chapter number 12
Book title
Understanding and Interpreting Machine Learning in Medical Image Computing Applications
Published in
Lecture notes in computer science, September 2018
DOI 10.1007/978-3-030-02628-8_12
Book ISBNs
978-3-03-002627-1, 978-3-03-002628-8
Authors

Sérgio Pereira, Raphael Meier, Victor Alves, Mauricio Reyes, Carlos A. Silva, Pereira, Sérgio, Meier, Raphael, Alves, Victor, Reyes, Mauricio, Silva, Carlos A.

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X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 121 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 121 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 14%
Student > Ph. D. Student 16 13%
Student > Master 14 12%
Student > Doctoral Student 9 7%
Student > Bachelor 9 7%
Other 13 11%
Unknown 43 36%
Readers by discipline Count As %
Computer Science 34 28%
Engineering 26 21%
Medicine and Dentistry 3 2%
Physics and Astronomy 2 2%
Neuroscience 2 2%
Other 5 4%
Unknown 49 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 April 2021.
All research outputs
#20,537,234
of 23,108,064 outputs
Outputs from Lecture notes in computer science
#7,016
of 8,147 outputs
Outputs of similar age
#297,667
of 342,070 outputs
Outputs of similar age from Lecture notes in computer science
#7
of 11 outputs
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So far Altmetric has tracked 8,147 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.

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