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 …

Brain age in neuroimaging has emerged over the last decade and reflects the estimated age
based on the brain MRI scan from a person. As a person ages, their brain structure will …

The poor explainability of deep learning models has hindered their adoption in safety and
quality-critical applications. This paper focuses on image classification models and aims to …

Most deep learning models for temporal regression directly output the estimation based on
single input images, ignoring the relationships between different images. In this paper, we …

The clinical applications of brain age prediction have expanded, particularly in anticipating
the onset and prognosis of various neurodegenerative diseases. In the current study, we …

Abstract Purpose To review eXplainable Artificial Intelligence/(XAI) methods available for
medical imaging/(MI). Method A scoping review was conducted following the Joanna Briggs …

The concept of 'brain age', derived from neuroimaging data, serves as a crucial biomarker
reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine …

Deep learning models have revolutionized numerous fields, yet their decision-making
processes often remain opaque, earning them the characterization of “black-box” models …

Estimation of human age is important in the fields of forensic medicine and the detection of
neurodegenerative diseases of the brain. Particularly, the age estimation methods using …

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 …