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 …

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 …

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

Deformable image registration is a fundamental step for medical image analysis. Recently
transformers have been used for registration and outperformed Convolutional Neural …

Unsupervised deep learning techniques have gained increasing popularity in deformable
medical image registration However, existing methods usually overlook the optimal similarity …

Deformable image registration is a fundamental requirement for medical image analysis.
Recently, transformers have been widely used in deep learning-based registration methods …

Image registration plays a crucial role in histological image analysis, encompassing tasks
like multi-modality fusion and disease grading. Traditional registration methods optimize …

Deformable image registration plays a crucial role in medical imaging, aiding in disease
diagnosis and image-guided interventions. Traditional iterative methods are slow, while …

Deformable image registration (DIR) is crucial for many medical image applications. In
recent years, learning-based methods utilizing the convolutional neural network (CNN) or …