| Title |
Data-efficient and weakly supervised computational pathology on whole-slide images
|
|---|---|
| Published in |
Nature Biomedical Engineering, March 2021
|
| DOI | 10.1038/s41551-020-00682-w |
| Pubmed ID | |
| Authors |
Ming Y. Lu, Drew F. K. Williamson, Tiffany Y. Chen, Richard J. Chen, Matteo Barbieri, Faisal Mahmood |
X Demographics
The data shown below were collected from the profiles of 72 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 | 30 | 42% |
| United Kingdom | 4 | 6% |
| Australia | 2 | 3% |
| Italy | 2 | 3% |
| Canada | 2 | 3% |
| Germany | 2 | 3% |
| Austria | 1 | 1% |
| Japan | 1 | 1% |
| Sweden | 1 | 1% |
| Other | 7 | 10% |
| Unknown | 20 | 28% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 33 | 46% |
| Members of the public | 31 | 43% |
| Practitioners (doctors, other healthcare professionals) | 6 | 8% |
| Science communicators (journalists, bloggers, editors) | 2 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 589 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 589 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 96 | 16% |
| Student > Master | 60 | 10% |
| Researcher | 59 | 10% |
| Student > Doctoral Student | 26 | 4% |
| Student > Postgraduate | 17 | 3% |
| Other | 71 | 12% |
| Unknown | 260 | 44% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 129 | 22% |
| Engineering | 58 | 10% |
| Medicine and Dentistry | 38 | 6% |
| Biochemistry, Genetics and Molecular Biology | 33 | 6% |
| Agricultural and Biological Sciences | 11 | 2% |
| Other | 47 | 8% |
| Unknown | 273 | 46% |
Attention Score in Context
This research output has an Altmetric Attention Score of 56. 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 14 March 2024.
All research outputs
#771,607
of 25,706,302 outputs
Outputs from Nature Biomedical Engineering
#424
of 1,152 outputs
Outputs of similar age
#22,104
of 453,647 outputs
Outputs of similar age from Nature Biomedical Engineering
#21
of 38 outputs
Altmetric has tracked 25,706,302 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,152 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 85.9. This one has gotten more attention than average, scoring higher than 63% 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 453,647 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 95% of its contemporaries.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.