| Title |
Deep-Learning-Based Semantic Labeling for 2D Mammography and Comparison of Complexity for Machine Learning Tasks
|
|---|---|
| Published in |
Journal of Digital Imaging, June 2019
|
| DOI | 10.1007/s10278-019-00244-w |
| Pubmed ID | |
| Authors |
Paul H. Yi, Abigail Lin, Jinchi Wei, Alice C. Yu, Haris I. Sair, Ferdinand K. Hui, Gregory D. Hager, Susan C. Harvey |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 25% |
| China | 1 | 13% |
| Netherlands | 1 | 13% |
| Unknown | 4 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 88% |
| Practitioners (doctors, other healthcare professionals) | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 72 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 11 | 15% |
| Student > Ph. D. Student | 7 | 10% |
| Student > Bachelor | 6 | 8% |
| Researcher | 6 | 8% |
| Student > Doctoral Student | 4 | 6% |
| Other | 7 | 10% |
| Unknown | 31 | 43% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 14 | 19% |
| Medicine and Dentistry | 12 | 17% |
| Engineering | 4 | 6% |
| Business, Management and Accounting | 1 | 1% |
| Agricultural and Biological Sciences | 1 | 1% |
| Other | 7 | 10% |
| Unknown | 33 | 46% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 22 April 2021.
All research outputs
#4,804,243
of 23,577,654 outputs
Outputs from Journal of Digital Imaging
#167
of 1,087 outputs
Outputs of similar age
#95,292
of 354,984 outputs
Outputs of similar age from Journal of Digital Imaging
#7
of 31 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,087 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done well, scoring higher than 84% 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 354,984 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.