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

Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Transformers have dominated the field of natural language processing and have recently
made an impact in the area of computer vision. In the field of medical image analysis …

Generative adversarial models with convolutional neural network (CNN) backbones have
recently been established as state-of-the-art in numerous medical image synthesis tasks …

Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical practice as each
contrast provides complementary information. However, the availability of each imaging …

With Artificial Intelligence (AI) increasingly permeating various aspects of society, including
healthcare, the adoption of the Transformers neural network architecture is rapidly changing …

Cross-modality magnetic resonance (MR) image synthesis can be used to generate missing
modalities from given ones. Existing (supervised learning) methods often require a large …

An increased interest in longitudinal neurodevelopment during the first few years after birth
has emerged in recent years. Noninvasive magnetic resonance imaging (MRI) can provide …

Existing approaches to 3D medical image segmentation can be generally categorized into
convolution-based or transformer-based methods. While convolutional neural networks …

This paper introduces a novel top-down representation approach for deformable image
registration which estimates the deformation field by capturing various short-and long-range …