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Found 10 papers, 4 papers with code
Three-Dimensional Medical Image Fusion with Deformable Cross-Attention
Furthermore, the prevailing methodologies are largely confined to fusing two-dimensional (2D) medical image slices, leading to a lack of contextual supervision in the fusion images and subsequently, a decreased information yield for physicians relative to three-dimensional (3D) images.
QUIZ: An Arbitrary Volumetric Point Matching Method for Medical Image Registration
Rigid pre-registration involving local-global matching or other large deformation scenarios is crucial.
Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains
In this paper, we propose an unsupervised MAR method based on the diffusion model, a generative model with a high capacity to represent data distributions.
XTransCT: Ultra-Fast Volumetric CT Reconstruction using Two Orthogonal X-Ray Projections for Image-guided Radiation Therapy via a Transformer Network
Computed tomography (CT) scans offer a detailed, three-dimensional representation of patients' internal organs.
Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising
The results demonstrate that our method outperforms the state-of-the-art unsupervised method and surpasses several supervised deep learning-based methods.
A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images
Deformable image registration is a necessary technique for fusing multi-modal pathology slices.
Elastic Net based Feature Ranking and Selection
After features are sorted according to their frequency, linear support vector machine performs the classification in an incremental manner.
CT Super Resolution via Zero Shot Learning
We incorporated the CT domain knowledge into the SADIR and unrolled it into a DL network (SADIR Net).
A Matlab Toolbox for Feature Importance Ranking
Moreover, the toolbox is evaluated on a database of 163 ultrasound images.
Performance Evaluation of Edge-Directed Interpolation Methods for Images
In this paper, performance of four edge-directed interpolation methods comparing with two traditional methods is evaluated on two groups of images.
