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

Aims The cerebellum is involved in higher‐order mental processing as well as sensorimotor
functions. Although structural abnormalities in the cerebellum have been demonstrated in …

Fibrosis has important pathoetiological and prognostic roles in chronic liver disease. This
study evaluates the role of radiomics in staging liver fibrosis. After literature search in …

Background To develop and compare machine learning models based on triphasic contrast-
enhanced CT (CECT) for distinguishing between benign and malignant renal tumors …

Objective This study investigated the impact of intolerance of uncertainty (IU) on structural
changes in the brain and symptom severity in patients with panic disorder. Methods This …

Brain dynamics are highly complex and yet hold the key to understanding brain function and
dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging …

Introduction Anxiety disorder is the most common psychiatric disorder among adolescents,
with generalized anxiety disorder (GAD) being a common subtype of anxiety disorder …

Many explainable AI methods for generating medical image saliency maps exist, but most
are devoted to working on trained neural network-based models. At the same time, many …

Background Radiomics is characterized by high-throughput extraction of texture features
from medical images for deep mining and analysis to establish meaningful associations …

Psychiatric disorders remain one of the most debilitating conditions; however, most patients
are never diagnosed and do not seek treatment. Despite its massive burden on modern …