Name
Abstract | ||
|---|---|---|
We investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive enhancement learning scheme for improved performance. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We formulate MC detectio... |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/TMI.2002.806569 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
Support vector machines,Testing,Object detection,Machine learning,Risk management,Detection algorithms,Image databases,Clustering algorithms,Error analysis,Biomedical imaging | Computer vision,Object detection,Receiver operating characteristic,Pattern recognition,Ranking SVM,Computer science,Word error rate,Support vector machine,Artificial intelligence,Structural risk minimization,Kernel method,Contextual image classification | Journal |
Volume | Issue | ISSN |
21 | 12 | 0278-0062 |
Citations | PageRank | References |
177 | 12.29 | 19 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Issam El-Naqa | 1 | 528 | 36.31 |
| Yongyi Yang | 2 | 1409 | 140.74 |
| Miles N. Wernick | 3 | 595 | 61.13 |
| Nikolas P. Galatsanos | 4 | 632 | 52.16 |
| Robert M Nishikawa | 5 | 599 | 58.25 |