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Computed Tomography (CT)

The term “computed tomography”, or CT, refers to a computerized x-ray imaging procedure in which a narrow beam of x-rays is aimed at a patient and quickly rotated around the body, producing signals that are processed by the machine's computer to generate cross-sectional images—or “slices”—of the body.

( Image credit: Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector )

Papers

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Showing 701–750 of 1207 papers

TitleStatusHype
Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation StudyCode0
A Rigid Registration Method in TEVAR—0
An Automated Approach for Timely Diagnosis and Prognosis of Coronavirus Disease—0
Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images—0
Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning—0
Free-form tumor synthesis in computed tomography images via richer generative adversarial networkCode0
Cycle-free CycleGAN using Invertible Generator for Unsupervised Low-Dose CT Denoising—0
Algorithm-driven Advances for Scientific CT Instruments: From Model-based to Deep Learning-based Approaches—0
Detecting COVID-19 and Community Acquired Pneumonia using Chest CT scan images with Deep LearningCode0
X2CT-FLOW: Maximum a posteriori reconstruction using a progressive flow-based deep generative model for ultra sparse-view computed tomography in ultra low-dose protocols—0
Atrous Residual Interconnected Encoder to Attention Decoder Framework for Vertebrae Segmentation via 3D Volumetric CT Images—0
Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT ImagesCode1
Fourier Image TransformerCode1
Multi-Feature Semi-Supervised Learning for COVID-19 Diagnosis from Chest X-ray ImagesCode0
IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction—0
High-quality Low-dose CT Reconstruction Using Convolutional Neural Networks with Spatial and Channel Squeeze and Excitation—0
Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks—0
Mixing-AdaSIN: Constructing a De-biased Dataset using Adaptive Structural Instance Normalization and Texture Mixing—0
Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition—0
MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising—0
Partial Matching in the Space of VarifoldsCode0
Scatter Correction in X-ray CT by Physics-Inspired Deep Learning—0
Model-based Reconstruction for Enhanced X-ray CT of Tri-structural Isotropic (TRISO) Particles—0
Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTsCode0
A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner DataCode1
Fusing Medical Image Features and Clinical Features with Deep Learning for Computer-Aided Diagnosis—0
Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation—0
Fibrosis-Net: A Tailored Deep Convolutional Neural Network Design for Prediction of Pulmonary Fibrosis Progression from Chest CT ImagesCode1
Attention-Enhanced Cross-Task Network for Analysing Multiple Attributes of Lung Nodules in CT—0
Interpolation of CT Projections by Exploiting Their Self-Similarity and Smoothness—0
Coarse-to-fine Airway Segmentation Using Multi information Fusion Network and CNN-based Region Growing—0
Kernel-based framework to estimate deformations of pneumothorax lung using relative position of anatomical landmarks—0
RCoNet: Deformable Mutual Information Maximization and High-order Uncertainty-aware Learning for Robust COVID-19 Detection—0
Classification of COVID-19 via Homology of CT-SCAN—0
Deep learning-based COVID-19 pneumonia classification using chest CT images: model generalizability—0
Noise Entangled GAN For Low-Dose CT Simulation—0
DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact ReductionCode1
Boosting Deep Transfer Learning for COVID-19 Classification—0
Plug-and-Play gradient-based denoisers applied to CT image enhancementCode0
Detection and severity classification of COVID-19 in CT images using deep learning—0
Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images—0
Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data—0
A multiscale model of vascular function in chronic thromboembolic pulmonary hypertension—0
D2A U-Net: Automatic Segmentation of COVID-19 Lesions from CT Slices with Dilated Convolution and Dual Attention MechanismCode1
A Real-World Demonstration of Machine Learning Generalizability: Intracranial Hemorrhage Detection on Head CT—0
Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep LearningCode0
Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted LossCode1
Few-shot Learning for CT Scan based COVID-19 Diagnosis—0
A fast method for simultaneous reconstruction and segmentation in X-ray CT application—0
Automated femur segmentation from computed tomography images using a deep neural network—0
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