An ROC comparison of four methods of combining information from multiple images of the same patient.
An ROC comparison of four methods of combining information from multiple images of the same patient.
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组合来自同一患者的多个图像的信息的四种方法的 ROC 比较。
DOI：
10.1118/1.1776674
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发表时间：
2004
期刊：
影响因子：
3.8
通讯作者：
Jiang,Yulei
中科院分区：
文献类型：
--
作者：
Liu,Bei;Metz,CharlesE;Jiang,Yulei
图像中包含的诊断信息的变化会降低诊断的准确性。原则上，获取同一患者的多个图像（例如，内侧倾斜和头尾视图乳房X光照片）可以帮助减少这种退化。我们演示了如何在计算机辅助诊断 (CAD) 的背景下实现这一点。假设从同一患者的多个图像获得的计算机输出可以单调地转换为同一对真值条件正态分布，并且为了简单起见，忽略图像之间的相关性，我们从理论上研究了组合计算机输出的四种方法：取平均值、中值、最大值或最小值。我们发现，正如人们所期望的那样，与单视图图像相比，平均值和中值总是会在受试者工作特征 (ROC) 曲线 (AUC) 下产生改善的面积，而平均值总是比中值产生更好的性能。然而，在某些情况下，最大值和最小值也可以产生改进的 AUC，并且在某些条件下可以优于平均值。令人惊讶的是，我们发现正态分布决策变量的最大值和最小值产生接近双正态的 ROC 曲线。当同一患者有多个图像时，这些结果可用作尝试提高 CAD 功效的指南。
Variance of diagnostic information contained in an image degrades diagnostic accuracy. Acquiring multiple images of the same patient (e.g., mediolateral oblique and craniocaudal view mammograms) can, in principle, help reduce this degradation. We demonstrate how this can be accomplished in the context of computer‐aided diagnosis (CAD). Assuming that computer outputs obtained from multiple images of the same patient can be transformed monotonically to the same pair of truth‐conditional normal distributions and, for simplicity, ignoring correlation among images, we investigate theoretically four methods of combining the computer outputs: taking the average, the median, the maximum, or the minimum. We found, as one would expect, that both the average and the median always produce an improved area under the receiver operating characteristic (ROC) curve (AUC) compared to the single‐view images, while the average always produces better performance than the median. However, the maximum and minimum also can produce improved AUCs in some situations, and under certain conditions can outperform the average. Surprisingly, we found that the maximum and minimum of normally‐distributed decision variables produce nearly binormal ROC curves. These results can be used as a guide in attempting to increase the efficacy of CAD when multiple images are available from the same patient.