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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yongyi Yang Liyang Wei Nishikawa, R.M. |
| Copyright Year | 2007 |
| Description | Author affiliation: Illinois Inst. of Technol., Chicago (Yongyi Yang) |
| Abstract | In this paper we propose a microcalcification classification scheme, assisted by content-based mammogram retrieval, for breast cancer diagnosis. We recently developed a machine learning approach for mammogram retrieval where the similarity measure between two lesion mammograms is modeled after expert observers. In this work we investigate how to use retrieved similar cases as references to improve the performance of a numerical classifier. Our rationale is that by adap-tively incorporating local proximity information into a classifier, it can help improve its classification accuracy, thereby leading to an improved "second opinion" to radiologists. Our experimental results on a mammogram database demonstrate that the proposed retrieval-driven approach with an adaptive support vector machine (SVM) could improve the classification performance from 0.78 to 0.82 in terms of the area under the ROC curve. |
| File Size | 259938 |
| File Format | |
| ISBN | 9781424414369 |
| ISSN | 15224880 |
| DOI | 10.1109/ICIP.2007.4379750 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-09-16 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image retrieval Content based retrieval Breast cancer Support vector machines Support vector machine classification Information retrieval Biomedical engineering Machine learning Image databases Biomedical computing image retrieval microcalcification classification adaptive support vector machine |
| Content Type | Text |
| Resource Type | Article |
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