Background and objectives Artificial intelligence (AI) has branched out to various
applications in healthcare, such as health services management, predictive medicine …

With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …

Artificial Intelligence (AI) techniques of deep learning have revolutionized the disease
diagnosis with their outstanding image classification performance. In spite of the outstanding …

Deep learning has recently achieved great success in many visual recognition tasks.
However, the deep neural networks (DNNs) are often perceived as black-boxes, making …

Abstract Transparency in Machine Learning (ML), often also referred to as interpretability or
explainability, attempts to reveal the working mechanisms of complex models. From a …

Abstract The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion
analysis is an emerging field of research that has the potential to alleviate the burden and …

Manual diagnosis of skin cancer is time-consuming and expensive; therefore, it is essential
to develop automated diagnostics methods with the ability to classify multiclass skin lesions …

How does the accuracy of deep neural network models trained to classify clinical images of
skin conditions vary across skin color? While recent studies demonstrate computer vision …

Background Due to their ability to solve complex problems, deep neural networks (DNNs)
are becoming increasingly popular in medical applications. However, decision-making by …

Medical imaging systems installed in different hospitals and labs generate images in bulk,
which could support medics to analyze infections or injuries. Manual inspection becomes …