Abstract Machine learning approaches have significantly advanced the 3D medical images
analysis, such as the CT and MRI scans, which enables improved diagnosis and treatment …

Recently, vision-language representation learning has made remarkable advancements in
building up medical foundation models, holding immense potential for transforming the …

Purpose The hippocampus is organized in subfields (HSF) involved in learning and memory
processes and widely implicated in pathologies at different ages of life, from neonatal …

Perivascular spaces (PVSs) form a central component of the brain\'s waste clearance
system, the glymphatic system. These structures are visible on MRI images, and their …

Brain tumour segmentation (BTS) is crucial for diagnosis and treatment planning by
delineating tumour boundaries and subregions in multi‐modality bio‐imaging data. Several …

Purpose Semantic segmentation is one of the most significant tasks in medical image
computing, whereby deep neural networks have shown great success. Unfortunately …

Auto-segmentation is one of the critical and foundational steps for medical image analysis.
The quality of auto-segmentation techniques influences the efficiency of precision radiology …

Alzheimer's disease (AD) is a type of brain disease that affects a person's ability to perform
daily tasks. Modern neuroimaging techniques have made it possible to detect structural and …

Flexible Piezoelectric Energy Harvesters and Sensors A systematic and complete
discussion of the latest progress in flexible piezoelectric energy harvesting and sensing …

Objectives: Accurate preoperative prediction of the pathological grade of clear cell renal cell
carcinoma (ccRCC) is crucial for optimal treatment planning and patient outcomes. This …