关于使用残差误差作为异常分数的陷阱
On the Pitfalls of Using the Residual Error as Anomaly Score
AI总结：
本文揭示了医学图像异常定位中依赖残差图像作为异常分数存在的根本缺陷，并通过理论定义和实验系统分析了异常强度、纹理与模型重建误差之间的相互影响。
摘要：
当前许多最先进的医学图像异常定位方法依赖于计算潜在异常输入图像与其“健康”重建之间的残差图像。由于对未见异常区域的重建应当存在误差，因此会产生较大的残差，作为检测医学图像异常的分数。然而，这一假设没有考虑到所用机器学习模型的不完美重建所产生的残差。这类误差很容易掩盖感兴趣的残差，从而强烈质疑将残差图像用作评分函数的做法。我们的工作详细探讨了残差图像中的这一根本性问题。我们从理论上定义了该问题，并通过一系列实验深入评估了异常的强度和纹理与不完美重建影响之间的关系。代码和实验可在 https://github.com/FeliMe/residual-score-pitfalls 获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
