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 …

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 …

Existing image fusion methods are typically limited to aligned source images and have to
“tolerate” parallaxes when images are unaligned. Simultaneously, the large variances …

Medical image registration has become pivotal in recent years with the integration of various
imaging modalities like X-ray, ultrasound, MRI, and CT scans, enabling comprehensive …

Due to their extreme long-range modeling capability, vision transformer-based networks
have become increasingly popular in deformable image registration. We believe, however …

Deep-learning-based registration methods emerged as a fast alternative to conventional
registration methods. However, these methods often still cannot achieve the same …

Facial micro-expressions (MEs) refer to brief spontaneous facial movements that can reveal
a person's genuine emotion. They are valuable in lie detection, criminal analysis, and other …

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

Unsupervised image registration commonly adopts U-Net style networks to predict dense
displacement fields in the full-resolution spatial domain. For high-resolution volumetric …

U-Net style networks are commonly utilized in unsupervised image registration to predict
dense displacement fields, which for high-resolution volumetric image data is a resource …