  Recently, deep learning has achieved remarkable successes in medical image
analysis. Although deep neural networks generate clinically important
predictions, they have inherent uncertainty. Such uncertainty is a major
barrier to report these predictions with confidence. In this paper, we propose
a novel yet simple Bayesian inference approach called SoftDropConnect (SDC) to
quantify the network uncertainty in medical imaging tasks with gliomas
segmentation and metastases classification as initial examples. Our key idea is
that during training and testing SDC modulates network parameters continuously
so as to allow affected information processing channels still in operation,
instead of disabling them as Dropout or DropConnet does. When compared with
three popular Bayesian inference methods including Bayes By Backprop, Dropout,
and DropConnect, our SDC method (SDC-W after optimization) outperforms the
three competing methods with a substantial margin. Quantitatively, our proposed
method generates substantial improvements in prediction accuracy (by 3.4%,
2.5%, and 6.7% respectively for whole tumor segmentation in terms of dice
score; and by 11.7%, 3.9%, and 8.7% respectively for brain metastases
classification) and greatly reduced epistemic and aleatoric uncertainties. Our
approach promises to deliver better diagnostic performance and make medical AI
imaging more explainable and trustworthy.
