Name
Abstract | ||
|---|---|---|
Accurate detection of microcalcification (MC) clusters is an important problem in breast cancer diagnosis. In this paper, we propose the use of a recently developed machine learning technique - relevance vector machine (RVM) - for automatic detection of MCs in digitized mammograms. RVM is based on Bayesian estimation theory, and as a feature it can yield a decision function that depends on only a very small number of so-called relevance vectors. The proposed method is tested using a database of 141 clinical mammograms, and compared with a support vector machine (SVM) classifier, which we developed previously. It is demonstrated that the RVM classifier achieves essentially the same detection performance as the SVM classifier, but does so with a much sparser kernel representation. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time implementation. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICIP.2005.1529674 | ICIP (1) |
Keywords | Field | DocType |
mammography,relevance vector machine learning,bayes methods,learning (artificial intelligence),bayesian estimation theory,breast cancer diagnosis,mammograms,microcalcifications,computational complexity,cancer,computer-aided diagnosis,relevance vector machine,microcalcifications detection,medical diagnostic computing,support vector machine,machine learning,learning artificial intelligence | Structured support vector machine,Microcalcification,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Relevance vector machine,Classifier (linguistics),Margin classifier,Machine learning,Quadratic classifier,Computational complexity theory | Conference |
Volume | ISSN | ISBN |
1 | 1522-4880 | 0-7803-9134-9 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liyang Wei | 1 | 167 | 12.04 |
| Yongyi Yang | 2 | 1409 | 140.74 |
| Robert M Nishikawa | 3 | 599 | 58.25 |