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Found 7 papers, 1 papers with code
Attention-based 3D CNN with Multi-layer Features for Alzheimer's Disease Diagnosis using Brain Images
Structural MRI and PET imaging play an important role in the diagnosis of Alzheimer's disease (AD), showing the morphological changes and glucose metabolism changes in the brain respectively.
Multi-modal Graph Neural Network for Early Diagnosis of Alzheimer's Disease from sMRI and PET Scans
In this study, we demonstrate how brain networks can be created from sMRI or PET images and be used in a population graph framework that can combine phenotypic information with imaging features of these brain networks.
A fast and flexible algorithm for microstructure reconstruction combining simulated annealing and deep learning
The proposed algorithm is flexible and can complete training and reconstruction in a short time with only one two-dimensional image.
Multiscale reconstruction of porous media based on multiple dictionaries learning
Digital modeling of the microstructure is important for studying the physical and transport properties of porous media.
Accurate and Fast reconstruction of Porous Media from Extremely Limited Information Using Conditional Generative Adversarial Network
To overcome this shortcoming, in this study we proposed a deep learning-based framework for reconstructing full image from its much smaller sub-area(s).
CT-image Super Resolution Using 3D Convolutional Neural Network
Super resolution (SR) methods based on deep learning have achieved surprising performance in two-dimensional (2D) images.
CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks
Experiments on compressed images produced by JPEG (we take the JPEG as an example in this paper) demonstrate that the proposed CISRDCNN yields state-of-the-art SR performance on commonly used test images and imagesets.
