| Title |
Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics
|
|---|---|
| Published in |
IEEE Transactions on Medical Imaging, August 2018
|
| DOI | 10.1109/tmi.2018.2865671 |
| Pubmed ID | |
| Authors |
Jun Zhang, Ashirbani Saha, Zhe Zhu, Maciej A. Mazurowski |
X Demographics
The data shown below were collected from the profiles of 11 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 | 8 | 73% |
| Switzerland | 1 | 9% |
| Poland | 1 | 9% |
| Unknown | 1 | 9% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 55% |
| Scientists | 3 | 27% |
| Practitioners (doctors, other healthcare professionals) | 1 | 9% |
| Unknown | 1 | 9% |
Mendeley readers
The data shown below were compiled from readership statistics for 136 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 136 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 15% |
| Student > Master | 18 | 13% |
| Researcher | 11 | 8% |
| Student > Bachelor | 10 | 7% |
| Professor > Associate Professor | 8 | 6% |
| Other | 16 | 12% |
| Unknown | 53 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 29 | 21% |
| Engineering | 19 | 14% |
| Medicine and Dentistry | 8 | 6% |
| Agricultural and Biological Sciences | 5 | 4% |
| Biochemistry, Genetics and Molecular Biology | 4 | 3% |
| Other | 8 | 6% |
| Unknown | 63 | 46% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. 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 20 March 2024.
All research outputs
#2,259,546
of 25,611,630 outputs
Outputs from IEEE Transactions on Medical Imaging
#77
of 3,765 outputs
Outputs of similar age
#42,706
of 325,574 outputs
Outputs of similar age from IEEE Transactions on Medical Imaging
#6
of 45 outputs
Altmetric has tracked 25,611,630 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,765 research outputs from this source. They receive a mean Attention Score of 4.8. 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 325,574 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.