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Exploitation of Correspondence Between CC and MLO Views in Computer Aided Mass Detection

机译:在计算机辅助大规模检测中的CC与MLO视图之间的对应关系的开发

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In this paper we investigate the effect of reclassification of CAD findings using correspondences in MLO and CC views, with the aim of reducing false positives and inconsistencies. We use a method to link regions identified as suspicious in both projections and add a two-view classifier to an existing CAD scheme. The input of this two-view classifier was a feature vector containing the likelihood of malignancy of the region, the likelihood of malignancy of the corresponding region in the other view, and a number of features that describe the resemblance between the both regions. Using FROC analysis we show that detection results improve when using two-view information.
机译:在本文中,我们使用MLO和CC视图中的相应探讨了CAD调查结果的重新分类的影响,目的是降低假阳性和不一致。我们使用一种将识别的区域链接在两个投影中标识为可疑的区域,并将双视图分类器添加到现有的CAD方案。该双视图分类器的输入是包含该区域恶性肿瘤的可能性的特征向量,在另一视图中,相应区域的恶性肿瘤的可能性,以及描述两个区域之间相似的特征。使用FROC分析,我们显示使用双视图信息时的检测结果改善。

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