Explaining Uncertainty in Multiple Sclerosis Lesion Segmentation Beyond Prediction Errors
Journal:
arXiv
Published Date:
Apr 7, 2025
Abstract
Trustworthy artificial intelligence (AI) is essential in healthcare,
particularly for high-stakes tasks like medical image segmentation. Explainable
AI and uncertainty quantification significantly enhance AI reliability by
addressing key attributes such as robustness, usability, and explainability.
Despite extensive technical advances in uncertainty quantification for medical
imaging, understanding the clinical informativeness and interpretability of
uncertainty remains limited. This study introduces a novel framework to explain
the potential sources of predictive uncertainty, specifically in cortical
lesion segmentation in multiple sclerosis using deep ensembles. The proposed
analysis shifts the focus from the uncertainty-error relationship towards
relevant medical and engineering factors. Our findings reveal that
instance-wise uncertainty is strongly related to lesion size, shape, and
cortical involvement. Expert rater feedback confirms that similar factors
impede annotator confidence. Evaluations conducted on two datasets (206
patients, almost 2000 lesions) under both in-domain and distribution-shift
conditions highlight the utility of the framework in different scenarios.
