Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …

Complicated deformation problems are frequently encountered in medical image
registration tasks. Although various advanced registration models have been proposed …

Background and objective Non-rigid image registration plays a significant role in computer-
aided diagnosis and surgical navigation for brain diseases. Registration methods that utilize …

The Transformer structures have been widely used in computer vision and have recently
made an impact in the area of medical image registration. However, the use of Transformer …

In this paper, we propose a novel dual-branch Transformation-Synthesis network (TS-Net),
for video motion retargeting. Given one subject video and one driving video, TS-Net can …

Medical images are often characterized by their structured anatomical representations and
spatially inhomogeneous contrasts. Leveraging anatomical priors in neural networks can …

Deep learning-based image registration (DLIR) methods have achieved remarkable
success in deformable image registration. We observe that iterative inference can exploit the …

Transformers have recently shown promise for medical image applications, leading to an
increasing interest in develo** such models for medical image registration. Recent …

We propose a Multi-stage Registration Network Using Intensity Adjustment, Dual-Stream
and Cost Volume (IDC-Net) for large deformation diffeomorphic image registration. Unlike …