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Machine Learning in Medical Imaging

Overview of attention for book
Machine Learning in Medical Imaging
Springer International Publishing
Attention for Chapter: Knowledge-Guided Multiview Deep Curriculum Learning for Elbow Fracture Classification
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (51st percentile)

Mentioned by

twitter
1 X user

Citations

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10 Dimensions

Readers on

mendeley
9 Mendeley
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Chapter title
Knowledge-Guided Multiview Deep Curriculum Learning for Elbow Fracture Classification
Book title
Machine Learning in Medical Imaging
Published in
arXiv, September 2021
DOI 10.1007/978-3-030-87589-3_57
Book ISBNs
978-3-03-087588-6, 978-3-03-087589-3
Authors

Luo, Jun, Kitamura, Gene, Arefan, Dooman, Doganay, Emine, Panigrahy, Ashok, Wu, Shandong, Jun Luo, Gene Kitamura, Dooman Arefan, Emine Doganay, Ashok Panigrahy, Shandong Wu

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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.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 22%
Librarian 1 11%
Student > Bachelor 1 11%
Student > Postgraduate 1 11%
Unknown 4 44%
Readers by discipline Count As %
Computer Science 1 11%
Agricultural and Biological Sciences 1 11%
Energy 1 11%
Medicine and Dentistry 1 11%
Engineering 1 11%
Other 0 0%
Unknown 4 44%
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 22 October 2021.
All research outputs
#15,686,478
of 23,310,485 outputs
Outputs from arXiv
#382,171
of 961,048 outputs
Outputs of similar age
#248,115
of 432,911 outputs
Outputs of similar age from arXiv
#14,713
of 35,216 outputs
Altmetric has tracked 23,310,485 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 961,048 research outputs from this source. They receive a mean Attention Score of 3.9. This one has gotten more attention than average, scoring higher than 53% 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 432,911 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 35,216 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.