Name
Title | ||
|---|---|---|
Characterization of myocardial motion patterns by unsupervised multiple kernel learning. |
Abstract | ||
|---|---|---|
•Multiple myocardial velocity patterns from a stress protocol are jointly analyzed.•Unsupervised Multiple Kernel Learning is used to reduce the complexity of the data.•The variability analysis on the learnt space unravels healthy/diseased differences.•The joint analysis of multiple patterns notably improves the characterization. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.media.2016.06.007 | Medical Image Analysis |
Keywords | Field | DocType |
Myocardial motion,Echocardiography,Multiple kernel learning,Pattern analysis | Pattern recognition,Multiple kernel learning,Pattern analysis,Curse of dimensionality,Unsupervised learning,Artificial intelligence,Objective method,Mathematics,Kernel regression,Stress Echocardiography,Heart failure with preserved ejection fraction | Journal |
Volume | ISSN | Citations |
35 | 1361-8415 | 2 |
PageRank | References | Authors |
0.42 | 17 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sergio Sanchez-Martinez | 1 | 2 | 1.10 |
| N Duchateau | 2 | 199 | 20.53 |
| Tamás Erdei | 3 | 2 | 0.76 |
| Alan Fraser | 4 | 2 | 0.76 |
| Bart H. Bijnens | 5 | 164 | 27.56 |
| G Piella | 6 | 366 | 43.86 |