Uncertainty quantification (UQ) has become critical for evaluating the reliability of artificial intelligence systems, especially in medical image segmentation. This study addresses the interpretability of instance-wise uncertainty values in deep learning models for focal lesion segmentation in magnetic resonance imaging, specifically cortical lesion (CL) segmentation in multiple sclerosis. CL segmentation presents several challenges, including the complexity of manual segmentation, high variability in annotation, data scarcity, and class imbalance, all of which contribute to aleatoric and epistemic uncertainty. We explore how UQ can be used not only to assess prediction reliability but also to provide insights into model behavior, detect biases, and verify the accuracy of UQ methods. Our research demonstrates the potential of instance-wise uncertainty values to offer post hoc global model explanations, serving as a sanity check for the model. The implementation is available at https://github.com/NataliiaMolch/interpret-lesion-unc.

本研究探讨了深度学习模型中实例级不确定性值的可解释性，以评估人工智能系统的可靠性，特别是在磁共振成像中进行皮质损伤分割的焦点损伤分割。研究结果显示实例级不确定性值的潜力，可以提供后期全局模型解释，并作为模型的一种验证方法。

不确定性的可解释性：多发性硬化症中皮层损伤分割的探索