  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.
