Simple Summary Deep learning has succeeded greatly in medical image-based cancer
diagnosis. To help readers better understand the current research status and ideas, this …

The lack of annotated medical images limits the performance of deep learning models,
which usually need large-scale labelled datasets. Few-shot learning techniques can reduce …

An effective backbone network is important to deep learning-based Deformable Medical
Image Registration (DMIR), because it extracts and matches the features between two …

Learning inter-image similarity is crucial for 3D medical images self-supervised pre-training,
due to their sharing of numerous same semantic regions. However, the lack of the semantic …

Deep learning has seen rapid growth in recent years and achieved state-of-the-art
performance in a wide range of applications. However, training models typically requires …

Deformation decomposition serves as a good solution for deformable image registration
when the deformation is large. Current deformation decomposition methods can be …

Deformable medical image registration can achieve fast and accurate alignment between
two images, enabling medical professionals to analyze images of different subjects in a …

Orthogonal 2D cervical vertebra (C-vertebra) X-ray images have the advantages of high
imaging efficiency, low radiation risk, easy operation and low cost for rapid primary clinical …

Deformable image registration aims to find a dense non-linear spatial correspondence
between a pair of images, which is a crucial step for many medical tasks such as tumor …

In this work, we address the task of few-shot medical image segmentation (MIS) with a novel
proposed framework based on the learning registration to learn segmentation (LRLS) …