The unprecedented growth of computational capabilities in recent years has allowed
Artificial Intelligence (AI) models to be developed for medical applications with remarkable …

Explainable artificial intelligence (XAI) has gained much interest in recent years for its ability
to explain the complex decision-making process of machine learning (ML) and deep …

eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of
machine learning models into a more digestible form. These methods help to communicate …

Abstract Explainable Artificial Intelligence refers to developing artificial intelligence models
and systems that can provide clear, understandable, and transparent explanations for their …

Abstract Context: Artificial Intelligence (AI) in the medical domain has achieved remarkable
results on various metrics primarily due to recent advancements in computational …

This work represents a first step towards a systematic analysis of the impact of the choice of
the baseline signals to be used in explainable baseline-dependent methods for multi-modal …

Explainable Artificial Intelligence (XAI) provides tools to help understanding how AI models
work and reach a particular decision or outcome. It helps to increase the interpretability of …

Objective: Brain microstructural changes already occur in the earliest phases of Alzheimer's
disease (AD) as evidenced in diffusion magnetic resonance imaging (dMRI) literature. This …

Explainable Artificial Intelligence (XAI) methods help to understand the internal mechanism
of machine learning models and how they reach a specific decision or made a specific …

Dementia's impact on cognitive function necessitates timely diagnosis for effective
intervention. Understanding the need for timely detection, the proposed work integrates …