Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation

arXiv:2311.08931v2 Announce Type: replace
Abstract: This paper explores uncertainty quantification (UQ) as an indicator of the trustworthiness of automated deep-learning (DL) tools in the context of white matter lesion (WML) segmentation from magnetic resonance imaging (MRI) scans of multiple sclerosis (MS) patients. Our study focuses on two principal aspects of uncertainty in structured output segmentation tasks. Firstly, we postulate that a good uncertainty measure should indicate predictions likely to be incorrect with high uncertainty values. Second, we investigate the merit …

analysis arxiv cs.cv deep learning matter segmentation type uncertainty