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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures

Overview of attention for book
Cover of 'Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures'

Table of Contents

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    Book Overview
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    Chapter 1 Probabilistic Surface Reconstruction with Unknown Correspondence
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    Chapter 2 Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty
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    Chapter 3 Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference
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    Chapter 4 Reg R-CNN: Lesion Detection and Grading Under Noisy Labels
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    Chapter 5 Fast Nonparametric Mutual-Information-based Registration and Uncertainty Estimation
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    Chapter 6 Quantifying Uncertainty of Deep Neural Networks in Skin Lesion Classification
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    Chapter 7 A Generalized Approach to Determine Confident Samples for Deep Neural Networks on Unseen Data
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    Chapter 8 Out of Distribution Detection for Intra-operative Functional Imaging
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    Chapter 9 A Clinical Measuring Platform for Building the Bridge Across the Quantification of Pathological N-Cells in Medical Imaging for Studies of Disease
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    Chapter 10 Spatiotemporal Statistical Model of Anatomical Landmarks on a Human Embryonic Brain
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    Chapter 11 Spaciousness Filters for Non-contrast CT Volume Segmentation of the Intestine Region for Emergency Ileus Diagnosis
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    Chapter 12 Recovering Physiological Changes in Nasal Anatomy with Confidence Estimates
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    Chapter 13 Synthesis of Medical Images Using GANs
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    Chapter 14 DPANet: A Novel Network Based on Dense Pyramid Feature Extractor and Dual Correlation Analysis Attention Modules for Colon Glands Segmentation
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    Chapter 15 Multi-instance Deep Learning with Graph Convolutional Neural Networks for Diagnosis of Kidney Diseases Using Ultrasound Imaging
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    Chapter 16 Data Augmentation from Sketch
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    Chapter 17 An Automated CNN-based 3D Anatomical Landmark Detection Method to Facilitate Surface-Based 3D Facial Shape Analysis
  19. Altmetric Badge
    Chapter 18 A Device-Independent Novel Statistical Modeling for Cerebral TOF-MRA Data Segmentation
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    Chapter 19 Three-Dimensional Face Reconstruction from Uncalibrated Photographs: Application to Early Detection of Genetic Syndromes
Attention for Chapter 8: Out of Distribution Detection for Intra-operative Functional Imaging
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About this Attention Score

  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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2 X users

Citations

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4 Dimensions

Readers on

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19 Mendeley
Summary X Dimensions citations
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Chapter title
Out of Distribution Detection for Intra-operative Functional Imaging
Chapter number 8
Book title
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures
Published in
arXiv, October 2019
DOI 10.1007/978-3-030-32689-0_8
Book ISBNs
978-3-03-032688-3, 978-3-03-032689-0
Authors

Tim J. Adler, Leonardo Ayala, Lynton Ardizzone, Hannes G. Kenngott, Anant Vemuri, Beat P. Müller-Stich, Carsten Rother, Ullrich Köthe, Lena Maier-Hein, Adler, Tim J., Ayala, Leonardo, Ardizzone, Lynton, Kenngott, Hannes G., Vemuri, Anant, Müller-Stich, Beat P., Rother, Carsten, Köthe, Ullrich, Maier-Hein, Lena

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X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 37%
Student > Master 3 16%
Professor 1 5%
Student > Bachelor 1 5%
Researcher 1 5%
Other 1 5%
Unknown 5 26%
Readers by discipline Count As %
Computer Science 7 37%
Physics and Astronomy 3 16%
Engineering 3 16%
Medicine and Dentistry 1 5%
Unknown 5 26%
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 08 November 2019.
All research outputs
#19,594,120
of 24,099,692 outputs
Outputs from arXiv
#580,062
of 1,020,419 outputs
Outputs of similar age
#266,750
of 355,378 outputs
Outputs of similar age from arXiv
#16,727
of 28,921 outputs
Altmetric has tracked 24,099,692 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,020,419 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 25th percentile – i.e., 25% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 355,378 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28,921 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.

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