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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

Overview of attention for book
Cover of 'Uncertainty for Safe Utilization of Machine Learning in Medical Imaging'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 MOrphologically-Aware Jaccard-Based ITerative Optimization (MOJITO) for Consensus Segmentation
  3. Altmetric Badge
    Chapter 2 Quantification of Predictive Uncertainty via Inference-Time Sampling
  4. Altmetric Badge
    Chapter 3 How certain are your uncertainties?
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    Chapter 4 On the Pitfalls of Entropy-Based Uncertainty for Multi-class Semi-supervised Segmentation
  6. Altmetric Badge
    Chapter 5 What Do Untargeted Adversarial Examples Reveal in Medical Image Segmentation?
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    Chapter 6 Improved Post-hoc Probability Calibration for Out-of-Domain MRI Segmentation
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    Chapter 7 Improving Error Detection in Deep Learning Based Radiotherapy Autocontouring Using Bayesian Uncertainty
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    Chapter 8 Stochastic Weight Perturbations Along the Hessian: A Plug-and-Play Method to Compute Uncertainty
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    Chapter 9 Calibration of Deep Medical Image Classifiers: An Empirical Comparison Using Dermatology and Histopathology Datasets
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    Chapter 10 nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods
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    Chapter 11 Generalized Probabilistic U-Net for Medical Image Segementation
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    Chapter 12 Joint Paraspinal Muscle Segmentation and Inter-rater Labeling Variability Prediction with Multi-task TransUNet
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    Chapter 13 Information Gain Sampling for Active Learning in Medical Image Classification
Attention for Chapter 3: How certain are your uncertainties?
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
6 X users

Citations

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1 Dimensions

Readers on

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3 Mendeley
Summary X Dimensions citations
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Chapter title
How certain are your uncertainties?
Chapter number 3
Book title
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging
Published in
arXiv, September 2022
DOI 10.1007/978-3-031-16749-2_3
Book ISBNs
978-3-03-116748-5, 978-3-03-116749-2
Authors

Luke Whitbread, Mark Jenkinson, Whitbread, Luke, Jenkinson, Mark

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 33%
Student > Master 1 33%
Unknown 1 33%
Readers by discipline Count As %
Medicine and Dentistry 1 33%
Unknown 2 67%
Attention Score in Context

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 20 September 2022.
All research outputs
#14,095,539
of 24,093,053 outputs
Outputs from arXiv
#219,358
of 1,020,419 outputs
Outputs of similar age
#174,864
of 418,853 outputs
Outputs of similar age from arXiv
#8,412
of 37,492 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% 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 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 418,853 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 57% of its contemporaries.
We're also able to compare this research output to 37,492 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.

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