Management of brain tumors is based on clinical and radiological information with
presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of …

Brain tumor segmentation is one of the most challenging problems in medical image
analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain …

Deep learning (DL) methods have recently become state-of-the-art in most automated
medical image segmentation tasks. Some of the biggest challenges in this field are related …

Automated brain tumor segmentation is crucial for aiding brain disease diagnosis and
evaluating disease progress. Currently, magnetic resonance imaging (MRI) is a routinely …

Existing deep neural networks for brain tumor segmentation from multimodal MRIs rely
predominantly on standard segmentation architectures, overlooking the underlying rules in …

The brain tumor is a kind of disease that does great harm to human health. Therefore, the
localization and segmentation of brain tumor images have always been an active field of …

This paper targets on self-supervised tumor segmentation. We make the following
contributions:(i) we take inspiration from the observation that tumors are often characterised …

Brain tumor segmentation in medical imaging is a critical task for diagnosis and treatment
while preserving patient data privacy and security. Traditional centralized approaches often …

Gliomas are the most common and aggressive form of all brain tumors, with medial survival
rates of less than two years for the highest grade. While accurate and reproducible …

Abstract RGB-Thermal (RGB-T) semantic segmentation provides the pixel-level prediction of
surrounding environments for autonomous vehicles and mobile robots in harsh conditions …