In domains such as medical and healthcare, the interpretability and explainability of
machine learning and artificial intelligence systems are crucial for building trust in their …

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few
years, artificial intelligence (AI) based approaches have been used to detect and grade DR …

Medical artificial intelligence (AI) systems have been remarkably successful, even
outperforming human performance at certain tasks. There is no doubt that AI is important to …

Vision Transformers have recently emerged as a competitive architecture in image
classification. The tremendous popularity of this model and its variants comes from its high …

Context: Recently, the machine learning (ML) field has been rapidly growing, mainly owing
to the availability of historical datasets and advanced computational power. This growth is …

Explainable artificial intelligence (XAI) is essential for enabling clinical users to get informed
decision support from AI and comply with evidence-based medical practice. Applying XAI in …

Deep neural networks (DNNs) have transformed the field of computer vision and currently
constitute some of the best models for representations learned via hierarchical processing in …

Deep neural networks (DNNs) have achieved physician-level accuracy on many imaging-
based medical diagnostic tasks, for example classification of retinal images in …

Background The lack of explanations for the decisions made by deep learning algorithms
has hampered their acceptance by the clinical community despite highly accurate results on …

Pain is a complex term that describes various sensations that create discomfort in various
ways or types inside the human body. Generally, pain has consequences that range from …