Wei Liyang, Yang Yongyi, Nishikawa Roberts M
Department of Electrical and Computer Engineering, Illinois Institute of Technology, 3301 South Dearborn Street, Chicago, IL 60616.
Pattern Recognit. 2009 Jun;42(6):1126-1132. doi: 10.1016/j.patcog.2008.08.028.
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.
在本文中，我们提出了一种借助基于内容的乳房X光图像检索辅助的微钙化分类方案，用于乳腺癌诊断。我们最近开发了一种用于乳房X光图像检索的机器学习方法，其中两个病变乳房X光图像之间的相似性度量是根据专家观察者的判断建模的。在这项工作中，我们研究如何使用检索到的相似病例作为参考来提高数值分类器的性能。我们的基本原理是，通过将局部邻近信息自适应地纳入分类器，可以帮助提高其分类准确性，从而为放射科医生提供更好的“第二意见”。我们在一个乳房X光图像数据库上的实验结果表明，所提出的基于检索的方法与自适应支持向量机（SVM）相结合，在ROC曲线下面积方面，可将分类性能从0.78提高到0.82。
