Deep learning shows great promise for medical image analysis but often lacks
explainability, hindering its adoption in healthcare. Attribution techniques that explain model …

Deep learning shows promise for medical image analysis but lacks interpretability, hindering
adoption in healthcare. Attribution techniques that explain model reasoning may increase …

Saliency visualization methods help explain artificial intelligence (AI) models and build the
trust of AI-driven medical image analysis applications. However, the trustworthiness of the …

Along with the increase in the accuracy of artificial intelligence systems, complexity has also
risen. Despite high accuracy, high-risk decision-making requires explanations about the …

Purpose To determine whether saliency maps in radiology artificial intelligence (AI) are
vulnerable to subtle perturbations of the input, which could lead to misleading …

Deep learning is increasingly gaining rapid adoption in healthcare to help improve patient
outcomes. This is more so in medical image analysis which requires extensive training to …

Deep learning models have been increasingly applied to medical images for tasks such as
lesion detection, segmentation, and diagnosis. However, the field suffers from the lack of …

Clinical applicability of automated decision support systems depends on a robust, well-
understood classification interpretation. Artificial neural networks while achieving class …

The rapid advancements in deep learning technologies have profoundly influenced the field
of medical image analysis, yet their full integration into clinical radiology practices has not …

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 …