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 …

Deep learning methods have been very effective for a variety of medical diagnostic tasks
and have even outperformed human experts on some of those. However, the black-box …

Deep learning has demonstrated remarkable performance in the medical domain, with
accuracy that rivals or even exceeds that of human experts. However, it has a significant …

Deep learning as performed by artificial deep neural networks (DNNs) has achieved great
successes recently in many important areas that deal with text, images, videos, graphs, and …

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

As artificial intelligence (AI) systems begin to make their way into clinical radiology practice,
it is crucial to assure that they function correctly and that they gain the trust of experts …

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 …

AI systems are quickly being adopted in radiology and, in general, in healthcare. A myriad of
systems is being proposed and developed on a daily basis for high-stake decisions that can …

Artificial Intelligence (AI) for the biomedical domain is gaining significant interest and holds
considerable potential for the future of healthcare, particularly also in the context of in vitro …

Objectives This study aims to contribute to an understanding of the explainability of
computer aided diagnosis studies in radiology that use end-to-end deep learning by …