Artificial intelligence (AI)—the ability of a machine to perform cognitive tasks to achieve a
particular goal based on provided data—is revolutionizing and reshaping our health-care …

Computer-aided diagnosis (CAD) in medicine is the result of a large amount of effort
expended in the interface of medicine and computer science. As some CAD systems in …

Training segmentation models for medical images continues to be challenging due to the
limited availability of data annotations. Segment Anything Model (SAM) is a foundation …

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 …

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

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

Multiple medical institutions collaboratively training a model using federated learning (FL)
has become a promising solution for maximizing the potential of data-driven models, yet the …

Abstract Model generalization capacity at domain shift (eg, various imaging protocols and
scanners) is crucial for deep learning methods in real-world clinical deployment. This paper …

Deep learning models usually suffer from the domain shift issue, where models trained on
one source domain do not generalize well to other unseen domains. In this work, we …

Automated prostate segmentation in MRI is highly demanded for computer-assisted
diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress …