| Title |
A Vector Machine Formulation with Application to the Computer-Aided Diagnosis of Breast Cancer from DCE-MRI Screening Examinations
|
|---|---|
| Published in |
Journal of Digital Imaging, July 2013
|
| DOI | 10.1007/s10278-013-9621-8 |
| Pubmed ID | |
| Authors |
Jacob E. D. Levman, Ellen Warner, Petrina Causer, Anne L. Martel |
| Abstract |
This study investigates the use of a proposed vector machine formulation with application to dynamic contrast-enhanced magnetic resonance imaging examinations in the context of the computer-aided diagnosis of breast cancer. This paper describes a method for generating feature measurements that characterize a lesion's vascular heterogeneity as well as a supervised learning formulation that represents an improvement over the conventional support vector machine in this application. Spatially varying signal-intensity measures were extracted from the examinations using principal components analysis and the machine learning technique known as the support vector machine (SVM) was used to classify the results. An alternative vector machine formulation was found to improve on the results produced by the established SVM in randomized bootstrap validation trials, yielding a receiver-operating characteristic curve area of 0.82 which represents a statistically significant improvement over the SVM technique in this application. |
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 % |
|---|---|---|
| Spain | 1 | 2% |
| Canada | 1 | 2% |
| Unknown | 54 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 21% |
| Researcher | 7 | 13% |
| Student > Bachelor | 6 | 11% |
| Unspecified | 5 | 9% |
| Professor | 3 | 5% |
| Other | 13 | 23% |
| Unknown | 10 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 19 | 34% |
| Computer Science | 7 | 13% |
| Engineering | 6 | 11% |
| Physics and Astronomy | 3 | 5% |
| Agricultural and Biological Sciences | 2 | 4% |
| Other | 7 | 13% |
| Unknown | 12 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 12 July 2013.
All research outputs
#5,855,450
of 22,714,025 outputs
Outputs from Journal of Digital Imaging
#232
of 1,045 outputs
Outputs of similar age
#48,617
of 194,246 outputs
Outputs of similar age from Journal of Digital Imaging
#4
of 4 outputs
Altmetric has tracked 22,714,025 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 1,045 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done well, scoring higher than 77% 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 194,246 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 74% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.