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Found 8 papers, 2 papers with code
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation
Source-free domain adaptation (SFDA) aims to adapt models trained on a labeled source domain to an unlabeled target domain without the access to source data.
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.
DECOR-NET: A COVID-19 Lung Infection Segmentation Network Improved by Emphasizing Low-level Features and Decorrelating Features
Since 2019, coronavirus Disease 2019 (COVID-19) has been widely spread and posed a serious threat to public health.
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.
Uncertainty Quantification in Medical Image Segmentation with Multi-decoder U-Net
We exploit the medical image segmentation uncertainty quantification by measuring segmentation performance with multiple annotations in a supervised learning manner and propose a U-Net based architecture with multiple decoders, where the image representation is encoded with the same encoder, and segmentation referring to each annotation is estimated with multiple decoders.
SeFM: A Sequential Feature Point Matching Algorithm for Object 3D Reconstruction
3D reconstruction is a fundamental issue in many applications and the feature point matching problem is a key step while reconstructing target objects.
