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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis

Overview of attention for book
Cover of 'Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Image Registration via Stochastic Gradient Markov Chain Monte Carlo
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    Chapter 2 RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation
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    Chapter 3 Hierarchical Brain Parcellation with Uncertainty
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    Chapter 4 Quantitative Comparison of Monte-Carlo Dropout Uncertainty Measures for Multi-class Segmentation
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    Chapter 5 Uncertainty Estimation in Landmark Localization Based on Gaussian Heatmaps
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    Chapter 6 Weight Averaging Impact on the Uncertainty of Retinal Artery-Venous Segmentation
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    Chapter 7 Improving Pathological Distribution Measurements with Bayesian Uncertainty
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    Chapter 8 Improving Reliability of Clinical Models Using Prediction Calibration
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    Chapter 9 Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior
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    Chapter 10 Uncertainty Estimation for Assessment of 3D US Scan Adequacy and DDH Metric Reliability
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    Chapter 11 Clustering-Based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain Templates
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    Chapter 12 Detection of Discriminative Neurological Circuits Using Hierarchical Graph Convolutional Networks in fMRI Sequences
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    Chapter 13 Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders
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    Chapter 14 Multi-scale Profiling of Brain Multigraphs by Eigen-Based Cross-diffusion and Heat Tracing for Brain State Profiling
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    Chapter 15 Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation
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    Chapter 16 Min-Cut Max-Flow for Network Abnormality Detection: Application to Preterm Birth
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    Chapter 17 Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface
  19. Altmetric Badge
    Chapter 18 The GraphNet Zoo: An All-in-One Graph Based Deep Semi-supervised Framework for Medical Image Classification
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    Chapter 19 Intraoperative Liver Surface Completion with Graph Convolutional VAE
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    Chapter 20 HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification
Attention for Chapter 19: Intraoperative Liver Surface Completion with Graph Convolutional VAE
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
10 Mendeley
Summary Twitter Dimensions citations
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Chapter title
Intraoperative Liver Surface Completion with Graph Convolutional VAE
Chapter number 19
Book title
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis
Published in
arXiv, October 2020
DOI 10.1007/978-3-030-60365-6_19
Book ISBNs
978-3-03-060364-9, 978-3-03-060365-6
Authors

Simone Foti, Bongjin Koo, Thomas Dowrick, Joao Ramalhinho, Moustafa Allam, Brian Davidson, Danail Stoyanov, Matthew J. Clarkson, João Ramalhinho, Foti, Simone, Koo, Bongjin, Dowrick, Thomas, Ramalhinho, João, Allam, Moustafa, Davidson, Brian, Stoyanov, Danail, Clarkson, Matthew J.

View on publisher site

Twitter Demographics

The data shown below were collected from the profiles of 5 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 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 20%
Researcher 2 20%
Student > Master 1 10%
Student > Doctoral Student 1 10%
Professor > Associate Professor 1 10%
Other 1 10%
Unknown 2 20%
Readers by discipline Count As %
Computer Science 4 40%
Engineering 4 40%
Unknown 2 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 14 July 2021.
All research outputs
#13,455,468
of 23,237,082 outputs
Outputs from arXiv
#214,281
of 957,911 outputs
Outputs of similar age
#199,938
of 415,286 outputs
Outputs of similar age from arXiv
#7,214
of 34,309 outputs
Altmetric has tracked 23,237,082 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 957,911 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done well, scoring higher than 76% of its peers.
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 415,286 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 34,309 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.

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