Since the emergence of the Covid-19 pandemic in late 2019, medical imaging has been
widely used to analyze this disease. Indeed, CT-scans of the lungs can help diagnose …

Coronavirus disease 2019 (COVID-19) has become a severe global pandemic. Accurate
pneumonia infection segmentation is important for assisting doctors in diagnosing COVID …

Federated learning (FL) is a distributed machine learning technique that enables
collaborative model training while avoiding explicit data sharing. The inherent privacy …

The domain gap caused mainly by variable medical image quality renders a major obstacle
on the path between training a segmentation model in the lab and applying the trained …

Purpose Segmenting medical images accurately and reliably is important for disease
diagnosis and treatment. It is a challenging task because of the wide variety of objects' sizes …

Automatic segmentation of ground glass opacities and consolidations in chest computer
tomography (CT) scans can potentially ease the burden of radiologists during times of high …

Image segmentation is the process of splitting a digital image into distinct segments or
categories based on shared characteristics like texture, color, and intensity. Its primary aim is …

Coronavirus disease 2019 (COVID-19) generated a global public health emergency since
December 2019, causing huge economic losses. To help radiologists strengthen their …

Background: In the field of biomedical imaging, radiomics is a promising approach that aims
to provide quantitative features from images. It is highly dependent on accurate identification …

Masked image modeling (MIM) with transformer backbones has recently been exploited as a
powerful self-supervised pre-training technique. The existing MIM methods adopt the …