NDLI: Relevance vector machine learning for detection of microcalcifications in mammograms

Content Provider	IEEE Xplore Digital Library
Author	Liyang Wei Yongyi Yang Nishikawa, R.M.
Copyright Year	2005
Description	Author affiliation: Dept. of Biomedical Eng., Illinois Inst. of Technol., Chicago, IL, USA (Liyang Wei; Yongyi Yang)
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.
File Size	128501
File Format	PDF
ISBN	0780391349
DOI	10.1109/ICIP.2005.1529674
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-14
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning Support vector machines Support vector machine classification Cancer detection Breast cancer Bayesian methods Estimation theory Testing Spatial databases Kernel relevance vector machine Computer-aided diagnosis microcalcifications
Content Type	Text
Resource Type	Article

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