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Found 8 papers, 3 papers with code
Deep Few-view High-resolution Photon-counting Extremity CT at Halved Dose for a Clinical Trial
Despite the success of deep learning methods for 2D few-view reconstruction, applying them to HR volumetric reconstruction of extremity scans for clinical diagnosis has been limited due to GPU memory constraints, training data scarcity, and domain gap issues.
Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation
Ultra-high resolution images are desirable in photon counting CT (PCCT), but resolution is physically limited by interactions such as charge sharing.
On Expressivity and Trainability of Quadratic Networks
Despite promising results so far achieved by networks of quadratic neurons, there are important issues not well addressed.
Suppression of Correlated Noise with Similarity-based Unsupervised Deep Learning
Limited by the independent noise assumption, current unsupervised denoising methods cannot process correlated noises as in CT images.
Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction
Deep neural network based methods have achieved promising results for CT metal artifact reduction (MAR), most of which use many synthesized paired images for training.
X-ray Photon-Counting Data Correction through Deep Learning
The simulated PCD data and the ground truth counterparts are then fed to a specially designed deep adversarial network for PCD data correction.
On Interpretability of Artificial Neural Networks: A Survey
Deep learning as represented by the artificial deep neural networks (DNNs) has achieved great success in many important areas that deal with text, images, videos, graphs, and so on.
Soft Autoencoder and Its Wavelet Adaptation Interpretation
Recently, deep learning becomes the main focus of machine learning research and has greatly impacted many important fields.
