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X Demographics
Mendeley readers
Attention Score in Context
Chapter title |
Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-Fine Framework and Its Adversarial Examples
|
---|---|
Chapter number | 4 |
Book title |
Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
|
Published in |
arXiv, January 2019
|
DOI | 10.1007/978-3-030-13969-8_4 |
Book ISBNs |
978-3-03-013968-1, 978-3-03-013969-8
|
Authors |
Yingwei Li, Zhuotun Zhu, Yuyin Zhou, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan L. Yuille, Li, Yingwei, Zhu, Zhuotun, Zhou, Yuyin, Xia, Yingda, Shen, Wei, Fishman, Elliot K., Yuille, Alan L. |
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.
Geographical breakdown
Country | Count | As % |
---|---|---|
Netherlands | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 50% |
Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 27 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 5 | 19% |
Student > Master | 4 | 15% |
Student > Ph. D. Student | 3 | 11% |
Student > Bachelor | 2 | 7% |
Librarian | 1 | 4% |
Other | 2 | 7% |
Unknown | 10 | 37% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 4 | 15% |
Engineering | 4 | 15% |
Linguistics | 1 | 4% |
Agricultural and Biological Sciences | 1 | 4% |
Immunology and Microbiology | 1 | 4% |
Other | 3 | 11% |
Unknown | 13 | 48% |
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 02 November 2020.
All research outputs
#18,692,168
of 23,164,913 outputs
Outputs from arXiv
#542,445
of 952,818 outputs
Outputs of similar age
#325,813
of 438,525 outputs
Outputs of similar age from arXiv
#15,705
of 24,448 outputs
Altmetric has tracked 23,164,913 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 952,818 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 28th percentile – i.e., 28% 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 438,525 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 24,448 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.