SoftDropConnect (SDC) -- Effective and Efficient Quantification of the Network Uncertainty in Deep MR Image Analysis. (arXiv:2201.08418v1 [eess.IV])

Web: http://arxiv.org/abs/2201.08418

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 …

analysis arxiv deep network uncertainty