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Found 6 papers, 2 papers with code
PET Image Denoising via Text-Guided Diffusion: Integrating Anatomical Priors through Text Prompts
Denoising diffusion probabilistic models (DDPMs) have demonstrated promising performance for PET image denoising.
LDM-Morph: Latent diffusion model guided deformable image registration
Finally, a hierarchical metric was proposed to evaluate the similarity of image pairs in both the original pixel space and latent-feature space, enhancing topology preservation while improving registration accuracy.
Fast Diffeomorphic Image Registration using Patch based Fully Convolutional Networks
Diffeomorphic image registration is a fundamental step in medical image analysis, owing to its capability to ensure the invertibility of transformations and preservation of topology.
HNAS-reg: hierarchical neural architecture search for deformable medical image registration
Convolutional neural networks (CNNs) have been widely used to build deep learning models for medical image registration, but manually designed network architectures are not necessarily optimal.
BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images
Moreover, both low-level and high-level features from the aforementioned three branches, including shape, size, boundary, and signed directional distance map of FAZ, are fused hierarchically with features from the diagnostic classifier.
Brain segmentation based on multi-atlas guided 3D fully convolutional network ensembles
To address this limitation, we trained a 3D FCN model for each ROI using patches of adaptive size and embedded outputs of the convolutional layers in the deconvolutional layers to further capture the local and global context patterns.
