Deep Neural Networks (DNNs) are known to be strong predictors, but their prediction
strategies can rarely be understood. With recent advances in Explainable Artificial …

The emerging field of eXplainable Artificial Intelligence (XAI) aims to bring transparency to
today's powerful but opaque deep learning models. While local XAI methods explain …

Applying traditional post-hoc attribution methods to segmentation or object detection
predictors offers only limited insights, as the obtained feature attribution maps at input level …

Explainable AI (XAI) is slowly becoming a key component for many AI applications. Rule-
based and modified backpropagation XAI approaches however often face challenges when …

Ensuring the quality of black-box Deep Neural Networks (DNNs) has become ever more
significant, especially in safety-critical domains such as automated driving. While global …

Objective: Identifying disability-related brain changes is important for multiple sclerosis (MS)
patients. Currently, there is no clear understanding about which pathological features drive …

Software reliability, a cornerstone in software engineering, has gained renewed attention
with the emergence of artificial intelligence (AI), spotlighting the significance of AI software …

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system.
Typical characteristics are multifocal inflammatory infiltration, demyelination, remyelination …