论文标题
探索实例级别的医疗检测不确定性
Exploring Instance-Level Uncertainty for Medical Detection
论文作者
论文摘要
深度学习以不确定性预测的能力被认为是其在临床常规中采用的关键。此外，通过根据经验证据对不确定性进行建模来实现绩效增长。尽管以前的工作已广泛讨论了分割和分类任务中的不确定性估计，但其在基于边界盒的检测中的应用是有限的，这主要是由于边界框对齐的挑战。在这项工作中，我们探索以增强具有两个不同的边界盒级（或实例级）不确定性估计值的2.5D检测CNN，即预测方差和蒙特卡洛（MC）样本方差。实验是在LUNA16数据集上进行肺结核检测的实验，该任务可以在结节和非结节之间存在重要的语义歧义。结果表明，通过利用两种方差的组合，我们的方法将评估分数从84.57％提高到88.86％。此外，与仅使用概率阈值相比，生成的不确定性可以实现出色的工作点，并且可以将性能进一步提高到89.52％。示例结节检测可视化，以进一步说明我们方法的优势。
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.
