Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely,
the small sample size problem. Many recent studies suggest using multi-domain data …

Medical image segmentation is a critical component in clinical practice, facilitating accurate
diagnosis, treatment planning, and disease monitoring. However, existing methods, often …

Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …

While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …

Abstract The Segment Anything Model (SAM), a foundation model for general image
segmentation, has demonstrated impressive zero-shot performance across numerous …

Federated learning allows distributed medical institutions to collaboratively learn a shared
prediction model with privacy protection. While at clinical deployment, the models trained in …

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 …

The use of AI systems in healthcare for the early screening of diseases is of great clinical
importance. Deep learning has shown great promise in medical imaging, but the reliability …

Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …