You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
A Self-Attention-Guided 3D Deep Residual Network With Big Transfer to Predict Local Failure in Brain Metastasis After Radiotherapy Using Multi-Channel MRI
|
|---|---|
| Published in |
IEEE Journal of Translational Engineering in Health and Medicine, November 2022
|
| DOI | 10.1109/jtehm.2022.3219625 |
| Pubmed ID | |
| Authors |
Seyed Ali Jalalifar, Hany Soliman, Arjun Sahgal, Ali Sadeghi-Naini |
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 % |
|---|---|---|
| France | 1 | 20% |
| United States | 1 | 20% |
| Canada | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 17 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 2 | 12% |
| Student > Ph. D. Student | 2 | 12% |
| Other | 1 | 6% |
| Lecturer | 1 | 6% |
| Student > Bachelor | 1 | 6% |
| Other | 2 | 12% |
| Unknown | 8 | 47% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 5 | 29% |
| Computer Science | 2 | 12% |
| Engineering | 1 | 6% |
| Unknown | 9 | 53% |
Attention Score in Context
This research output has an Altmetric Attention Score of 124. 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 29 December 2022.
All research outputs
#335,810
of 25,392,582 outputs
Outputs from IEEE Journal of Translational Engineering in Health and Medicine
#3
of 228 outputs
Outputs of similar age
#8,675
of 440,732 outputs
Outputs of similar age from IEEE Journal of Translational Engineering in Health and Medicine
#2
of 9 outputs
Altmetric has tracked 25,392,582 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 228 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has done particularly well, scoring higher than 98% 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 440,732 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 7 of them.