The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …

Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …

With the rapid development of deep reinforcement learning (DRL) techniques, there is an
increasing need to understand and interpret DRL policies. While recent research has …

In recent times, the progress of machine learning has facilitated the development of decision
support systems that exhibit predictive accuracy, surpassing human capabilities in certain …

Combining deep learning image analysis methods and large-scale imaging datasets offers
many opportunities to neuroscience imaging and epidemiology. However, despite these …

Existing studies on disease diagnostic models focus either on diagnostic model learning for
performance improvement or on the visual explanation of a trained diagnostic model. We …

An important goal of medical imaging is to be able to precisely detect patterns of disease
specific to individual scans; however, this is challenged in brain imaging by the degree of …

Identifying statistical trends for a wide range of practical power system applications,
including sustainable energy forecasting, demand response, energy decomposition, and …

The emerging field of geometric deep learning extends the application of convolutional
neural networks to irregular domains such as graphs, meshes and surfaces. Several recent …