Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …

An increasing array of tools is being developed using artificial intelligence (AI) and machine
learning (ML) for cancer imaging. The development of an optimal tool requires …

Foundation models in deep learning are characterized by a single large-scale model trained
on vast amounts of data serving as the foundation for various downstream tasks. Foundation …

Immunotherapy is used to treat almost all patients with advanced non-small cell lung cancer
(NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we …

Computer-extracted tumour characteristics have been incorporated into medical imaging
computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an …

Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …

Purpose Artificial intelligence (AI) models are playing an increasing role in biomedical
research and healthcare services. This review focuses on challenges points to be clarified …

Biomedical imaging is a driver of scientific discovery and a core component of medical care
and is being stimulated by the field of deep learning. While semantic segmentation …

Modern medicine is reliant on various medical imaging technologies for non-invasively
observing patients' anatomy. However, the interpretation of medical images can be highly …

Radiomics refers to the extraction of mineable data from medical imaging and has been
applied within oncology to improve diagnosis, prognostication, and clinical decision support …