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X Demographics
Mendeley readers
Attention Score in Context
Chapter title |
Learning Optimal Deep Projection of $$^{18}$$ F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes
|
---|---|
Chapter number | 26 |
Book title |
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
|
Published in |
arXiv, September 2018
|
DOI | 10.1007/978-3-030-00889-5_26 |
Book ISBNs |
978-3-03-000888-8, 978-3-03-000889-5
|
Authors |
Shubham Kumar, Abhijit Guha Roy, Ping Wu, Sailesh Conjeti, R. S. Anand, Jian Wang, Igor Yakushev, Stefan Förster, Markus Schwaiger, Sung-Cheng Huang, Axel Rominger, Chuantao Zuo, Kuangyu Shi, Kumar, Shubham, Roy, Abhijit Guha, Wu, Ping, Conjeti, Sailesh, Anand, R. S., Wang, Jian, Yakushev, Igor, Förster, Stefan, Schwaiger, Markus, Huang, Sung-Cheng, Rominger, Axel, Zuo, Chuantao, Shi, Kuangyu |
X Demographics
The data shown below were collected from the profiles of 3 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 | 33% |
Unknown | 2 | 67% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 67% |
Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 5 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 2 | 40% |
Professor | 1 | 20% |
Lecturer | 1 | 20% |
Researcher | 1 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 2 | 40% |
Physics and Astronomy | 1 | 20% |
Neuroscience | 1 | 20% |
Unknown | 1 | 20% |
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 16 October 2018.
All research outputs
#17,990,409
of 23,103,903 outputs
Outputs from arXiv
#444,755
of 949,614 outputs
Outputs of similar age
#245,210
of 342,063 outputs
Outputs of similar age from arXiv
#14,911
of 24,448 outputs
Altmetric has tracked 23,103,903 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 949,614 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 43rd percentile – i.e., 43% 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 342,063 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% 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 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.