Abstract
Recent advancements in machine learning have led to novel imaging systems and algorithms that address ill-posed problems. Assessing their trustworthiness and understanding how to deploy them safely at test time remains an important and open problem. We propose a method that leverages conformal prediction to retrieve upper/lower bounds and statistical inliers/outliers of reconstructions based on the prediction intervals of downstream metrics. We apply our method to sparse-view CT for downstream radiotherapy planning and show 1) that metric-guided bounds have valid coverage for downstream metrics while conventional pixel-wise bounds do not and 2) anatomical differences of upper/lower bounds between metric-guided and pixel-wise methods. Our work paves the way for more meaningful reconstruction bounds. Code available at this https URL
Abstract (translated)
近年来机器学习的进步导致了处理欠拟合问题的新颖成像系统和算法。评估它们的可信度和在测试时安全部署它们仍然是一个重要且开放的问题。我们提出了一种利用同构预测来检索基于预测间隔的重建的上/下界以及统计异常/正常化的方法。我们将该方法应用于稀疏视野CT downstream放射治疗计划，并证明了：在下游指标的指导下，指标指导的边界具有有效的覆盖范围，而传统的像素级边界则没有；以及指标指导和像素级方法的upper/lower边界之间解剖学差异。我们的工作为更有意义的研究奠定了基础。代码可在此处访问：https:// this URL
URL
https://arxiv.org/abs/2404.15274
