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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: GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation
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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 (70th percentile)

Mentioned by

twitter
4 X users

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
39 Mendeley
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Chapter title
GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation
Book title
Machine Learning in Medical Imaging
Published in
arXiv, September 2021
DOI 10.1007/978-3-030-87589-3_40
Book ISBNs
978-3-03-087588-6, 978-3-03-087589-3
Authors

Li, Yunxiang, Wang, Shuai, Wang, Jun, Zeng, Guodong, Liu, Wenjun, Zhang, Qianni, Jin, Qun, Wang, Yaqi, Yunxiang Li, Shuai Wang, Jun Wang, Guodong Zeng, Wenjun Liu, Qianni Zhang, Qun Jin, Yaqi Wang

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users 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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 7 18%
Student > Ph. D. Student 3 8%
Lecturer 2 5%
Student > Master 2 5%
Professor 1 3%
Other 2 5%
Unknown 22 56%
Readers by discipline Count As %
Computer Science 10 26%
Engineering 3 8%
Medicine and Dentistry 2 5%
Unknown 24 62%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 02 October 2021.
All research outputs
#14,879,188
of 24,093,053 outputs
Outputs from arXiv
#262,290
of 1,018,817 outputs
Outputs of similar age
#209,209
of 421,042 outputs
Outputs of similar age from arXiv
#9,184
of 34,558 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,018,817 research outputs from this source. They receive a mean Attention Score of 4.0. This one has gotten more attention than average, scoring higher than 71% 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 421,042 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 34,558 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 70% of its contemporaries.