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 …

Medical image analysis tasks are characterized by high-noise, volumetric, and multi-
modality, posing challenges for the model that attempts to learn robust features from the …

Abstract 4D medical images which represent 3D images with temporal information are
crucial in clinical practice for capturing dynamic changes and monitoring long-term disease …

Deformable image registration is an essential approach for medical image analysis. This
paper introduces MambaMorph, an innovative multi-modality deformable registration …

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 …

Deformable medical image registration plays an important role in many clinical applications.
It aims to find a dense deformation field to establish point-wise correspondences between a …

Most of the existing fusion algorithms are not robust to unregistered input images. Even after
image registration, nonlinear nonregistration may persist in the local areas of the images …

The differential diagnosis between atypical Parkinsonian syndromes may be challenging
and critical. We aimed to proposed a radiomics-guided deep learning (DL) model to …

Medical image registration is a fundamental and core technology in the field of medical
image processing and analysis. In recent years, deep learning-based registration methods …

Because the abstracts contain complex information and the labels of abstracts do not
contain information about categories, it is difficult for cognitive models to extract …