论文标题
计算组织病理学中分布外检测的不确定性估计
Uncertainty estimation for out-of-distribution detection in computational histopathology
论文作者
论文摘要
现在，在计算组织病理学算法中，现在在一系列任务上的表现要优于人类，但是迄今为止，在诊所中没有任何人用于自动诊断。在算法可以参与此类高风险决策之前，他们需要“知道何时不知道”，即他们需要估计其预测性不确定性。这使他们能够将潜在的错误预测推迟给人类病理学家，从而提高了他们的安全性。在这里，我们评估了临床组织病理学数据的几种不确定性估计方法的预测性能和校准。我们表明，远距离感知的不确定性估计方法优于常用方法，例如蒙特卡洛辍学和深层合奏。但是，我们观察到在所有不确定性估计方法中，对新样本的预测性能和校准下降。我们还研究了使用不确定性阈值以拒绝分布样本的选择性预测。我们证明了这种方法的局限性，并建议未来研究的领域。
In computational histopathology algorithms now outperform humans on a range of tasks, but to date none are employed for automated diagnoses in the clinic. Before algorithms can be involved in such high-stakes decisions they need to "know when they don't know", i.e., they need to estimate their predictive uncertainty. This allows them to defer potentially erroneous predictions to a human pathologist, thus increasing their safety. Here, we evaluate the predictive performance and calibration of several uncertainty estimation methods on clinical histopathology data. We show that a distance-aware uncertainty estimation method outperforms commonly used approaches, such as Monte Carlo dropout and deep ensembles. However, we observe a drop in predictive performance and calibration on novel samples across all uncertainty estimation methods tested. We also investigate the use of uncertainty thresholding to reject out-of-distribution samples for selective prediction. We demonstrate the limitations of this approach and suggest areas for future research.
