Search Results for author:
Found 4 papers, 1 papers with code
NexToU: Efficient Topology-Aware U-Net for Medical Image Segmentation
Motivated by the successful breakthroughs of graph neural networks (GNN) in capturing topological properties and non-Euclidean relationships across various fields, we propose NexToU, a novel hybrid architecture for medical image segmentation.
Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation
Based on the Denoising Diffusion Probabilistic Model (DDPM), medical image segmentation can be described as a conditional image generation task, which allows to compute pixel-wise uncertainty maps of the segmentation and allows an implicit ensemble of segmentations to boost the segmentation performance.
Multi-modal Dynamic Graph Network: Coupling Structural and Functional Connectome for Disease Diagnosis and Classification
Graph neural networks have been proven to be of great importance in modeling brain connectome networks and relating disease-specific patterns.
Estimating Brain Age with Global and Local Dependencies
The brain age has been proven to be a phenotype of relevance to cognitive performance and brain disease.
