论文标题
在使用残余误差作为异常得分的陷阱上
On the Pitfalls of Using the Residual Error as Anomaly Score
论文作者
论文摘要
许多用于医学图像中异常定位的最新方法依赖于在潜在的异常输入图像及其“健康”重建之间计算残留图像。由于不看到异常区域的重建应该是错误的，因此这会产生大量残留物作为检测医学图像异常的分数。但是，此假设并未考虑到所使用的机器学习模型不完善的重建所产生的残差。此类错误很容易掩盖关注的残留物，因此强烈质疑使用残留图像作为评分函数。我们的工作详细探讨了残留图像的基本问题。从理论上讲，我们定义了问题，并彻底评估了在一系列实验中对不完善重建的影响的强度和质地的影响。代码和实验可在https://github.com/felime/residual-score-pitfalls下获得
Many current state-of-the-art methods for anomaly localization in medical images rely on calculating a residual image between a potentially anomalous input image and its "healthy" reconstruction. As the reconstruction of the unseen anomalous region should be erroneous, this yields large residuals as a score to detect anomalies in medical images. However, this assumption does not take into account residuals resulting from imperfect reconstructions of the machine learning models used. Such errors can easily overshadow residuals of interest and therefore strongly question the use of residual images as scoring function. Our work explores this fundamental problem of residual images in detail. We theoretically define the problem and thoroughly evaluate the influence of intensity and texture of anomalies against the effect of imperfect reconstructions in a series of experiments. Code and experiments are available under https://github.com/FeliMe/residual-score-pitfalls
