The ability of deep learning to predict with uncertainty is recognized as key for its adoption in clinical routines. Moreover, performance gain has been enabled by modelling uncertainty according to empirical evidence. While previous work has widely discussed the uncertainty estimation in segmentation and classification tasks, its application on bounding-box-based detection has been limited, mainly due to the challenge of bounding box aligning. In this work, we explore to augment a 2.5D detection CNN with two different bounding-box-level (or instance-level) uncertainty estimates, i.e., predictive variance and Monte Carlo (MC) sample variance. Experiments are conducted for lung nodule detection on LUNA16 dataset, a task where significant semantic ambiguities can exist between nodules and non-nodules. Results show that our method improves the evaluating score from 84.57% to 88.86% by utilizing a combination of both types of variances. Moreover, we show the generated uncertainty enables superior operating points compared to using the probability threshold only, and can further boost the performance to 89.52%. Example nodule detections are visualized to further illustrate the advantages of our method.

翻译：深入学习预测不确定性的能力被公认为是临床常规采用这种能力的关键。此外,根据经验证据,通过模拟不确定性,提高了绩效。虽然以前的工作广泛讨论了分解和分类任务中的不确定性估计,但其对捆绑箱检测的应用有限,主要原因是捆绑盒对接的挑战。在这项工作中,我们探索用两种不同的捆绑箱(或例级)不确定性估计,即预测差异和蒙特卡洛(MC)样本差异,增强2.5D检测CNN。在LUNA16数据集上进行了肺结核检测实验,这一任务在结核和非结核之间可能存在严重的语义模糊性。结果显示,我们的方法通过使用两种差异的组合,将评分从84.57%提高到88.86%。此外,我们显示了产生的不确定性使得与仅使用概率阈值相比的运行点更优越,并能够进一步提升到89.52%的性能。一些结核检测实例被视觉化,以进一步说明我们的方法的优点。