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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Tracing retinal vessel trees by transductive inference
|
|---|---|
| Published in |
BMC Bioinformatics, January 2014
|
| DOI | 10.1186/1471-2105-15-20 |
| Pubmed ID | |
| Authors |
Jaydeep De, Huiqi Li, Li Cheng |
| Abstract |
Structural study of retinal blood vessels provides an early indication of diseases such as diabetic retinopathy, glaucoma, and hypertensive retinopathy. These studies require accurate tracing of retinal vessel tree structure from fundus images in an automated manner. However, the existing work encounters great difficulties when dealing with the crossover issue commonly-seen in vessel networks. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 20% |
| Norway | 1 | 20% |
| Germany | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 56 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 20% |
| Researcher | 8 | 14% |
| Student > Doctoral Student | 7 | 13% |
| Student > Master | 6 | 11% |
| Student > Bachelor | 3 | 5% |
| Other | 7 | 13% |
| Unknown | 14 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 19 | 34% |
| Engineering | 10 | 18% |
| Medicine and Dentistry | 8 | 14% |
| Agricultural and Biological Sciences | 2 | 4% |
| Biochemistry, Genetics and Molecular Biology | 1 | 2% |
| Other | 2 | 4% |
| Unknown | 14 | 25% |
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 17 September 2014.
All research outputs
#13,566,023
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#3,850
of 7,454 outputs
Outputs of similar age
#160,905
of 310,856 outputs
Outputs of similar age from BMC Bioinformatics
#46
of 99 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 45th percentile – i.e., 45% 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 310,856 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 99 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 52% of its contemporaries.