Deep learning for tomographic image reconstruction
Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …
intelligence and a new frontier of machine learning. Deep learning has been widely used in …
A robust volumetric transformer for accurate 3D tumor segmentation
We propose a Transformer architecture for volumetric segmentation, a challenging task that
requires kee** a complex balance in encoding local and global spatial cues, and …
requires kee** a complex balance in encoding local and global spatial cues, and …
CLEAR: comprehensive learning enabled adversarial reconstruction for subtle structure enhanced low-dose CT imaging
X-ray computed tomography (CT) is of great clinical significance in medical practice
because it can provide anatomical information about the human body without invasion …
because it can provide anatomical information about the human body without invasion …
Synergizing medical imaging and radiotherapy with deep learning
This article reviews deep learning methods for medical imaging (focusing on image
reconstruction, segmentation, registration, and radiomics) and radiotherapy (ranging from …
reconstruction, segmentation, registration, and radiomics) and radiotherapy (ranging from …
CoreDiff: Contextual error-modulated generalized diffusion model for low-dose CT denoising and generalization
Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon
starvation and electronic noise. Recently, some works have attempted to use diffusion …
starvation and electronic noise. Recently, some works have attempted to use diffusion …
Low‐dose CT reconstruction with Noise2Noise network and testing‐time fine‐tuning
Purpose Deep learning‐based image denoising and reconstruction methods demonstrated
promising performance on low‐dose CT imaging in recent years. However, most existing …
promising performance on low‐dose CT imaging in recent years. However, most existing …
Sam's net: a self-augmented multistage deep-learning network for end-to-end reconstruction of limited angle CT
Limited angle reconstruction is a typical ill-posed problem in computed tomography (CT).
Given incomplete projection data, images reconstructed by conventional analytical …
Given incomplete projection data, images reconstructed by conventional analytical …
Half2Half: deep neural network based CT image denoising without independent reference data
Reducing radiation dose of x-ray computed tomography (CT) and thereby decreasing the
potential risk to patients are desirable in CT imaging. Deep neural network (DNN) has been …
potential risk to patients are desirable in CT imaging. Deep neural network (DNN) has been …
Self-supervised dynamic CT perfusion image denoising with deep neural networks
Dynamic computed tomography perfusion (CTP) imaging is a promising approach for acute
ischemic stroke diagnosis and evaluation. Hemodynamic parametric maps of cerebral …
ischemic stroke diagnosis and evaluation. Hemodynamic parametric maps of cerebral …
Consensus deep neural networks for antenna design and optimization
We present a general approach for antenna design and optimization based on consensus of
results from a number of independently trained deep neural networks (DNNs). The aim of …
results from a number of independently trained deep neural networks (DNNs). The aim of …