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 …

Objective Employing transfer learning (TL) with convolutional neural networks (CNNs), well-
trained on non-medical ImageNet dataset, has shown promising results for medical image …

With age, the prevalence of diseases such as fatty liver disease, cirrhosis, and type two
diabetes increases. Approaches to both predict abdominal age and identify risk factors for …

Background Chest CT screening as supplementary means is crucial in diagnosing novel
coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning …

Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated
magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image …

XAI is a rapidly progressing field of machine learning, aiming to unravel the predictions of
complex models. XAI is especially required in sensitive applications, eg in health care, when …

Purpose Patient age has important clinical utility for refining a differential diagnosis in
radiology. Here, we evaluate the potential for convolutional neural network models to predict …

In a large-scale medical examination, the UK Biobank study has successfully imaged more
than 32,000 volunteer participants with magnetic resonance imaging (MRI). Each scan is …

Aging is an important risk factor for disease, leading to morphological change that can be
assessed on Computed Tomography (CT) scans. We propose a deep learning model for …

To study genetic factors associated with brain aging, we first need to quantify brain aging.
Statistical models have been created for estimating the apparent age of the brain, or …