Medical Vision-Language Pre-training (MedVLP) has made significant progress in enabling
zero-shot tasks for medical image understanding. However, training MedVLP models …

Background Minimizing radiation exposure is crucial in monitoring adolescent idiopathic
scoliosis (AIS). Generative adversarial networks (GANs) have emerged as valuable tools …

Medical image segmentation has recently demonstrated impressive progress with deep
neural networks, yet the heterogeneous modalities and scarcity of mask annotations limit the …

Purpose Advancements of deep learning in medical imaging are often constrained by the
limited availability of large, annotated datasets, resulting in underperforming models when …

Rationale and Objectives Magnetic resonance imaging (MRI) is a vital tool for diagnosing
neurological disorders, frequently utilising gadolinium-based contrast agents (GBCAs) to …

Deep learning-based medical image segmentation models, such as U-Net, rely on high-
quality annotated datasets to achieve accurate predictions. However, the increasing use of …

Clinical data is fundamental to advance neurosurgical research, but access is often
constrained by data availability, small sample sizes, privacy regulations, and resource …

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by joint
inflammation and progressive structural damage. Joint space width (JSW) is a critical …

Artificial Intelligence-Generated Content, a subset of Generative Artificial Intelligence, holds
significant potential for advancing the e-health sector by generating diverse forms of data. In …

Evaluating AI models in the field of medical imaging, particularly for tasks such as nodule
detection, is a challenging endeavor due to the scarcity of large, diverse, and well-annotated …