Journal
PATTERN RECOGNITION
Volume 42, Issue 6, Pages 1126-1132Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2008.08.028
Keywords
Microcalcification classification; Adaptive support vector machine; Image retrieval
Funding
- University of Chicago
- Hologic
- NATIONAL CANCER INSTITUTE [R21CA089668] Funding Source: NIH RePORTER
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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 was 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 adaptively incorporating local proximity information into a classifier. it can help to 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. (C) 2008 Elsevier Ltd. All rights reserved.
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