The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …

Research in artificial intelligence for radiology and radiotherapy has recently become
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …

In the last decade, convolutional neural networks (ConvNets) have been a major focus of
research in medical image analysis. However, the performances of ConvNets may be limited …

Deep learning has received extensive research interest in develo** new medical image
processing algorithms, and deep learning based models have been remarkably successful …

Federated Learning (FL) is a collaboratively decentralized privacy-preserving technology to
overcome challenges of data silos and data sensibility. Exactly what research is carrying the …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …

Artificial Intelligence (AI) solutions have been widely used in healthcare, and recent
developments in deep neural networks have contributed to significant advances in medical …

This paper presents a review of deep learning (DL)-based medical image registration
methods. We summarized the latest developments and applications of DL-based registration …

What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …